Naturalistic Study assessing the effectiveness of Human Givens Therapy

4 August

2016

| Dissertation

Cathriona Edwards, Student No: 85040119, Class: 8604, Word Count: 22,840 A naturalistic study to assess the effectiveness of Human Givens therapy for psychological distress in routine psychotherapeutic private practice in Ireland.

Supervisor: Philip James Course: MA CBT, Irish College of Humanities & Applied Sciences (ICHAS)

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Declaration I hereby declare that this is entirely my own work and that it has not been submitted as an exercise for the award of a degree at this or any other College or University. I agree that the library may lend or copy this dissertation on request.

Cathriona Edwards August 4 2016

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Abstract Many commentators acknowledge that the efficacy and effectiveness of psychotherapy has by now been established. The growth of practice-based research methodologies in the last decade - particularly benchmarking – however, offers the opportunity to build ecologically-valid research which simultaneously answers professional, societal and academic needs for effective and high-quality evidencebased treatment and practice. The purpose of this naturalistic study is to assess the effectiveness of Human Givens (HG) Psychotherapy in Ireland for the first time.

Replicating Andrews et al., (2011) pre- and post- treatment CORE-10 outcome measure scores were benchmarked against those of HG treatment effectiveness found in it’s UK primary care sample. Effect size (Cohen’s d) clinical equivalence was surpassed against the benchmark for all with valid scores and those clinical at inception (n=33, d=1.56; n=28; d=2.29 as benchmarked against n=107, d=1.27). This study reports 82% of the clinical population demonstrated ‘reliable improvement’ over an average of 3 sessions (n=25; M=12, SD=5.29). 71% of the clinical group recovered and reliably improved (n = 20; M= 12.2, SD=5.21), the largest percentage of recovery yet of Human Givens using similar measures, and over the shortest session number. Potential moderators of such change are explored before concluding that the study, albeit limited by the relatively small sample size, serves to reinforce the plausibility of HG Therapy in the effective and efficient treatment of psychological distress. Recommendations, including the further investigation of HG for State-sponsored intervention in primary care, as well as the adoption of routine outcome measurement to foster therapy effectiveness, therapist effectiveness, and practice-research databases from which future research questions could be explored, are offered.

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Acknowledgements The researcher wishes to acknowledge the hugely valuable and valued knowledge, support and expertise given by her supervisor, Philip James, in the design, proposal, completion and writing-up of this study. Thank you, Phil – I couldn’t have done it without you!

To my colleagues, Sue and Nick at the Dublin Human Givens Centre, a huge thank you for your willingness to participate and to include your own work in the study. It is deeply appreciated. To Bill: big thanks for your insight and the marvel that is Pragmatic Tracker. It was indispensible.

To all of our clients who were willing to trust us with their stories, and for those stories to be converted into the data upon which this study is based. It simply wouldn’t be without you – thank you.

Big thanks also to Carolyn, for sharing her wordsmith wisdom in wrestling with my errant commas and syntax. To my friends and family, thank you for the energizing and bolstering cheerleading throughout. And finally, to my boys, Greg and Glen, for your patience, tolerance, unfailing support, space afforded and love continually dispensed - words cannot capture my love and gratitude.

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Table of Contents Page Declaration Abstract Acknowledgements Table of Contents Background and Significance Introduction Human Givens Summary

ii iii iv v 1 1 3 5

Literature Review Introduction Efficacy & Effectiveness: The Historical Context Naturalistic and Effectiveness Studies Practice-Outcome Research, Outcome Measurement & Practice-Research Networks Human Givens Literature Review Summary

6 6 8 12 17 21 23

Methods Methodological Issues of Studies reviewed Purpose of the study Research Question Study design Hypothesis Population, Sample and Sampling Study Setting Data Collection Methods Measures Procedures Allocation to Therapists Therapists’ Briefing Client Briefing & Procedures Interventions Data Analysis Methods Benchmarking Pilot Study Issues of reliability and variability Ethical Considerations Methods Summary

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Table of Contents cont’d Page Findings Introduction Participant enrolment and outcome measurement flowchart Medication use Duration of presenting problems Clinical Outcomes Demographics Within-Subjects effectiveness Significance of Treatment Effect Size of Treatment Between Subjects Effectiveness Moderating Effects of Different Groups (Medication & Gender) Benchmark Clinical Population Reliable Change Recovery Client Satisfaction Therapist as a Moderator of Change

41 41

Discussion Introduction Results Overview relative to benchmark Methodological considerations Ending as a moderating influence Problem Duration Problem Type Problem Severity Dose-Effect Size Client Satisfaction: Common Factors Considered Therapist as a moderator of change Outcome Measurement Interventions

56 56

Limitations

76

Conclusion

78

Recommendations

79

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56 58 60 62 69 66 68 68 70 72 74

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Table of Contents cont’d

Page

Appendices

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Appendix 1.1: Human Givens Needs and Resources Appendix 1.2: Study Consent Form Appendix 1.3: Measure – CORE-10 Appendix 1.4: Measure – PRN F5 Appendix 1.5: Therapist Briefing Appendix 1.6: Study Briefing

81 83 84 85 87 89

References

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Tables and Figures Table 1: Illustration of problem presentation by client (n = 34) based on reported duration of symptoms at initial assessment Table 2: Pre- and Post- treatment CORE-10 scores for population with valid pre and post scores and effect sizes (Cohen’s d) by type of therapy ending and benchmark CORE-10 data from Andrews et al (2011) Table 3: compares Clinical population (ie with CORE-10 ≥11 at inception) with valid pre- and post measures compared to benchmark valid (but not clinical) pre- and post-treatment scores.

Table 4: Reliable Change and Recovery rates for this study ‘Dublin’ v benchmark, Andrews et al., 2011 ‘Luton’ study showing pre-and post treatment effectiveness for clients (n=28) who were clinical at pretreatment and made full recovery or reliably improved.

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Figure 1: Participant Enrolment & Outcome Measurement Flowchart Figure 2: Normal Probability Plot CORE10 T1 (pre-treatment) Figure 3: Normal Probability Plot CORE10 T2 (post-treatment) Figure 4: Normal Probability Plot CORE10 (T1-T2) Figure 5. The mean pre- and post-CORE-10 scores for clinical group planned (n = 24), unplanned (n = 4), all (n=28) (pre-treatment = T1, posttreatment = T2). (Clinical cut-off = 11)

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42 46 47 47 52

Background and Significance of the Study The World Health Organisation defines mental health as: “A state of complete physical, mental and social well-being, and not merely the absence of disease.”

(WHO, 2016).

According to Tedstone Doherty et al., (2008) approximately 12% of the Irish population experiences emotional or psychological distress at any given time and approximately 14% of Irish people reported difficulties with their mental health over a one-year period. McDaid (2013) found that “the most common mental health problems cited by GPs as presenting issues were anxiety disorders (49%), depression (24%) and emotional difficulties (20%)” (p.7).

The projected figures from the total population aged 18 years and over suggest that 320,381 people will attend the general practitioner for mental health problems, 160,190 people will attend outpatient clinics, 51,261 will attend day centres and 19,222 will use inpatient mental health facilities over a one-year period (Tedstone Doherty et al., 2008).

Research demonstrates that the diagnosis and treatment of psychological distress is often suboptimal, however (e.g., in Ireland, McDaid, 2013; in UK, Egede, 2007) and funding of mental health care provision in Ireland has been loudly criticised. As recently as June 2016, for example, Dr Shari McDaid, Director of Mental Health Reform criticised the overall funding of mental health in Ireland, as it “significantly lags behind best practice in other countries, for example in the UK, where mental health funding is at approximately 11 per cent of the overall health spend, compared to just 6.5 per cent here.” (McDaid, S., 8 June 2016, Mental Health Reform press release*).

Traditionally, the bulk of mental health care funding in Ireland has been concentrated on specialist mental health services rather than mental health provision in primary care. However, the primary care system addresses 90% of mental health difficulties in Ireland (McDaid, 2013) with GPs as gatekeepers for people in distress seeking respite (Kierans & Byrne, 2010). This may in part be why in a survey of those in receipt of mental health care in this country, respondents testified to a ‘dominance of

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medication’ as often the only option offered and ‘a lack of access to counselling’ (McDaid, 2013, p.2).

Reflecting an international trend for the need to make mental health services available through primary care (see WHO, 2014), Ireland is currently developing a primary care model (see HSE Mental Health Division Operational Plan 2015/2016**). The implementation of it has been patchy, however (‘Snapshot of the HSE Mental Health Division Operational Plan’, Mental Health Reform, Press Release, May, 2016***).

The HSE has instigated in part its ‘A Vision for Change’ policy (see HSE Mental Health Division Operational Plan 2015/2016). Over the past two years, it has been rolling out its ‘Counselling in Primary Care‘ programme (CIPC). This provides preventive counselling for a subscribed range of mental health difficulties deemed appropriate for brief-intervention treatment. An intervention aimed at medical card holders, it entitles them to up to eight free psychotherapy sessions with a professionally qualified and accredited Counsellor/Therapist. While the service had received almost 10,000 referrals between January and August 2014 alone, commentators underline that ‘there continues to be a huge shortage of access to counselling for people on low incomes.’ (‘Reaction to Programme for Government’, Mental Health Reform press release, May 2016****).

In a context in which the Irish Council for Psychotherapy (ICP) quote the adult outpatient psychotherapy need as ‘between 20-45 sessions being necessary for 50-75 per cent of psychotherapy clients to recover’ (as per Carr, 2007, p. vi) and it being the recognised accrediting body for the CIPC programme, the requirement to continue to develop and refine efficient and effective psychotherapeutic treatment options is clear.

Acting quickly prevents pre-clinical and clinical symptoms worsening and generating into conditions that become more difficult and take longer to treat (e.g., Kopta et al., 1994). Exploring alternative possible approaches that can be time-, and thereby, costeffective to apply in both public and private practice, I would argue, is an imperative.

Most importantly, it is an imperative that comes with a concomitant requirement that the interventions offered are evidence-based and supported by research, which is in line with WHO’s Mental Health Action plan 2013-2020 objective at a global level to ‘strengthen information systems, evidence and research for mental health’ (WHO, 2013. p.10).

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Building an evidence base to underpin best practice is not only responsible, from an ethical client care perspective, it is becoming essential in terms of professional and societal expectations and norms (see NICE, 2005; and ministerial commitment to regulation in Ireland by 2019****).

The evidence-base established in any and every single practice through routine collection of outcome measures from all clients is one such method (Castonguay et al., 2013; Andrews et al., 2013; Duncan et al., 2015). It serves multiple needs: it measures and moulds individual client outcomes but can also feed into practiceresearch-networks, through which results can be benchmarked against larger datasets (Minami et al, 2009; Andrews et al., 2013).

Practice-based research draws a virtuous circle: we have a national and international culture which increasingly demands accountability (McDaid, 2013; Mental Health Forum, 2016; WHO, 2014). This call for accountability can be combined with the use of developed and research-validated outcome measures [For example: CORE-OM; Barkham et al., 2001; PCOMS; Miller, Duncan, Sorrell, & Brown, 2005)]. Such outcome measurement also protects the client and helps to ensure a high standard of practice (Duncan et al., 2015). The resulting growing database facilitates benchmark measurement and spawns future research opportunities (e.g., Chow et al., 2015). It also drives effectiveness more directly too; the most recent studies have demonstrated that the utilisation of outcome measures can in themselves be levers of effectiveness (e.g., Duncan & Reese, 2015).

Human Givens Human Givens (HG) is an approach to the management and maintenance of emotional health and wellbeing. It’s basic principle is that we are all born with vital physical and emotional needs along with the innate resources to help us to fulfil those needs. The needs and resources together make up the ‘givens’ of human nature and are the means by which individuals can reach their genetic human potential (Griffin and Tyrrell, 2004). [Appendix 1.1: list of innate needs and resources]. When these emotional needs are not met, or when the innate resources are used incorrectly, individuals may experience mental distress (Griffin and Tyrell, 2004). The

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psychotherapy approach focuses on discovering the unmet needs of the client, understanding why they are not being met, and helping the client get them met in a healthy, balanced way.

The central organizing idea is also in part based on more recent evidence regarding the effects of high emotional arousal on the brain (LeDoux, 1998; Phan et al, 2002) how the brain processes trauma (Brewin & Holmes, 2003) how mindfulness interventions and practice generate cortical changes (Kabat-Zinn, 2003; Seigel, 2007) and HG theory on the role of Rapid Eye Movement (REM) sleep and dreaming (Griffin and Tyrrell, 2006). While the organizing ideas may have different emphasis and move away from prescriptive diagnosis, the HG approach still uses many empirically-grounded clinical interventions (Salkovskis, 2002).

The interventions and techniques employed have been adopted from other approaches,

including

cognitive

behavioural

therapy

(CBT)

neurolinguistic

programming, solution-focused therapy, hypnotherapy and motivational interviewing.

Therapeutic models consistently used include the rewind technique, guided imagery, reframing, relaxation techniques, metaphor, and post-hypnotic suggestion (Griffin and Tyrrell, 1998). HG has been shown to be effective in a general practice in the UK (Andrews et al., 2011) and in a five-year evaluation of its effectiveness across different work contexts, via a practice research network (Andrews et al., 2013). It generates results quickly with recovery reached within 5-6 sessions on average (Andrews et al., 2013).

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Background and Significance: Summary The purpose of this study is to assess the effectiveness of Human Givens (HG) Psychotherapy in Ireland for the first time. The aim is two-fold: to compare effectiveness in this population to that already conducted in the UK and to contribute to the evidence base for this relatively new approach.

In this way, confidence can be built in an approach demonstrated to be reliable and efficient in alleviating psychological distress. Moreover, a Random Controlled Trial (RCT) has yet to be conducted on Human Givens Therapy. Many researchers argue that an RCT of a new approach is necessary to establish efficacy (e.g., Minami et al., 2007). Growing evidence from effectiveness studies such as this one can add to the momentum and motivation to compel the generation and financing of an RCT. Effectiveness research is thus a step on the way. And it is a step that can generate meaningful, systematic and ecologically valid data to demonstrate effectiveness, against larger dataset standards within and outside of the HG approach, while contributing to therapist effectiveness and progression.

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Literature Review Introduction In order to explore and illustrate the context within which this study is conducted, a systematic review was conducted of literature across four relevant threads, as determined by this author:

1. ‘Effectiveness and Efficacy: The Historical Context’: A systematic literature review of effectiveness and efficacy studies of psychotherapeutic treatments was performed in order to form a baseline expectation of what the current study may deliver and the magnitude of change commonly observed in outcome studies.

Given the plethora of studies across different paradigms and methodologies, there was a heavy reliance on meta-analyses and commentaries. Databases mined included APAPsycNet, SciVerse Science Direct, PsychInfo, Medline and

keyword

searches

included:

‘Psychotherapy

Effectiveness’,

‘psychotherapy efficacy’, ‘therapy effectiveness’, ‘counselling effectiveness’, ‘therapy outcome studies’ psychotherapy outcome studies,’ ‘naturalistic studies’, ‘benchmark studies’, and variations thereof. Given the similarities between Human Givens and CBT, ‘Cognitive Behavioural Therapy’, ‘CBT’ alongside ‘effectiveness’ and ‘efficacy’ were also searched.

2. Naturalistic Studies The search then focussed upon exploring methodologies and relevant studies within the naturalistic paradigm. Keyword searches within the databases mentioned above included ‘naturalistic studies’, ‘clinically-based studies’ ‘psychotherapy in practice’, ‘Effectiveness studies’, ‘field effectiveness studies’ and variations thereof, as well as ‘evidence-based practice’, ‘brief therapy’, ‘effectiveness’. Key author searches then included ‘Norcross’, ‘Castonguay’, ‘Lambert’, ‘Beutler’, ‘Minami’, ‘Wampold’ etc.

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3. Practice-Outcome Research, Outcome Measurement and Practice-ResearchNetworks A deeper search into the paradigm of practice-outcome research was then undertaken including a search using keywords such as ‘Practice Outcome Research’, ‘Outcome Measures’, ‘Practice Research Networks’, ‘PracticeBased Evidence’, ‘benchmark studies’, ‘Routine Outcome Measures’, ‘Practice Measures’ etc. Key author searches included ‘Minami’, ‘Miller’, ‘Andrews’, ‘Duncan’, ‘Castonguay’, ‘Kraus’, ‘Wampold’ 4. Human Givens Therapy And of course to understand the immediate needs with regard to any research done on Human Givens therapy, searches were done using keywords ‘Human Givens’, ‘Brief Therapy’, combined with the word ‘effectiveness’ and ‘efficacy’ with author searches on ‘Griffin’ and ‘Tyrrell’, ‘Andrews’, ‘Clark’, ‘Minami’.

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Literature Review. Efficacy and Effectiveness: The Historical Context

In 1952, Hans Eysenck published his seminal article concluding that there was actually no evidence to demonstrate that psychotherapy was effective (Eysenck, 1952). And though there were ‘staggering research problems’ (Strupp, 1963, p.2) to be confronted, ‘the necessity of properly planned and executed experimental studies into this important field’ were paramount (Eysenck, 1964, p.97). How to achieve that aim however, is something that Eysenck (1964) and Strupp (1963) argued vehemently about at the time and their legacy has divided the evolution of empirical psychology since (see Eysenck, 1964; Strupp, 1963; for overview commentary Miller et al., 2013).

In Strupp’s words (1963, p.2), the field would ‘not be satisfied with studies of therapeutic outcomes until it succeeded in becoming more explicit about the independent variable’. Randomized control trials (RCTs) were considered the ‘gold standard’, the principal means of investigating relevant independent variables and studying psychotherapy outcome (Bower and King, 2000; Haaga and Stiles, 2000).

The central contention was that for psychotherapy to advance as a science, in an analogous process to medicine, it would be necessary to operationalize falsifiable hypotheses using specific methods (discrete independent variables) test those hypotheses, and promote the methods that stood up to replication and rigour (Gambrill, 1990; Zuriff, 1985). Indeed, according to the criteria set down by the Task Force of the American Psychological Association (APA) empirical support for psychotherapeutic methods could only be provided by RCTs, in which a therapy group was compared to a control condition or to an established therapy (Task Force on Promotion and Dissemination of Psychological Procedures, 1995; Chambless and Hollon, 1998; Chambless and Ollendick, 2001).

Throughout the 1980’s, scientific interest as measured in RCTs focussed on elements within psychotherapy that led to change, with much concentration on therapeutic alliance and other common factors of therapy (Bergin and Garfield, 1994; Bordin, 1979; Gaston, 1990; Horvath and Symonds, 1991). In the 1990’s, the field moved on

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to the evidence-based practice paradigm (Roth and Fonagy, 1996; Sackett, 1995; Sackett, 2002) which has exerted a large influence on treatment recommendations across psychiatric and psychological disorders in NICE guidelines in UK (NICE, 2005a; NICE, 2005b; NICE, 2006) in those established in the US (see APA, 2012), and in Canada (CPA; see Dezois et al., 2014) amongst others.

In overarching terms, with thousands of RCTs and effectiveness studies offering replication and advancement conducted, and consolidated by key meta-analyses (e.g., Smith & Glass, 1977), most commentators agree that both the efficacy and effectiveness of psychotherapy has indeed been demonstrated. Miller et al., (2013), asserts: “the efficacy and effectiveness of psychotherapy are well established. These are based on “standards stated and follow-ups carried out” (Eysenck, 1964, p. 99).

They benefit, Miller et al. continues, ‘from continual refinements of what constitutes effectiveness, whether in the behavioral terms preferred by Eysenck or the intrapsychic judgments of clients preferred by Strupp (Eid & Larsen, 2008)” (p.92).

Similarly, Castonguay, (2013), asserts that, “Today, the efficacy and effectiveness of psychotherapy (at least for the majority of clients who complete it) is no longer in question.” (Lambert & Ogles, 2004, p. 52). And Irish psychologist, Alan Carr (2007), asserts that meta-analyses of hundreds and thousands of outcome studies ‘show unequivocally that psychotherapy works’ (p. 20).

So, what does that ‘efficacy and effectiveness’ of psychotherapy look like? What magnitude of change has outcome studies commonly delivered?

Smith & Glass (1977) in their famous meta-analysis, systematically quantitatively synthesised the findings of 375 evaluations of psychotherapy. They concluded that the typical therapy client is better off than 75% of untreated individuals. They observed that the average effect size was 0.68 – considered to be a moderate to large effect size attributable to post-treatment outcome.

Since then, many meta-analyses have been conducted which have confirmed the efficacy of psychotherapy (Lambert & Ogle, 2004; Wampold, 2001; Weisz, 2004).

Shapiro & Shapiro, (1982) for example, conducted a meta-analysis of 143 studies of psychotherapy exclusively involving adult populations, all of which had been in the original Smith & Glass (1977) population. They found an overall effect size of 1.03,

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which suggests a very large post-treatment outcome. Similarly, reviews by Andrews & Harvey (1981) and Landman and Dawes (1982) warrant inclusion here as they also looked exclusively at adult populations – as does this study – and again at subsets of the populations examined in Smith & Glass (1977). In the former, a meta-analysis of 81 studies of clinically distressed adults, the overall effect size was .72 – a large effect. In the latter, a meta-analysis of 42 studies, which were marked by ‘methodological rigour’, they obtained an overall effect size of .9 (large). These findings underline and support Smith & Glass’s (1977) original meta-analysis, and indeed answer criticism that the inclusion of clinical and non-clinical cases, and a lack of methodological rigour, may have compromised the reliability of Smith & Glass’s (1977) original conclusions.

Similarly, a huge bank of efficacy research has demonstrated the efficacy of Cognitive Behavioural Treatment (CBT) specifically for the treatment of psychological distress. As the treatment type closest to Human Givens (see Griffin & Tyrrell, 2007) an overview of such findings is relevant.

In a meta-analysis of 332 studies of the effectiveness of CBT for 16 different disorders or populations, Butler et al., (2006) obtained a large mean weighted effect size of .95 for depression and a range of anxiety disorders. For bulimia, the mean prepost- treatment effect size of 1.27 was also very large. Moderate effect sizes were generated when looking at CBT for marital distress, anger control, and chronic pain in adults – and childhood somatic disorders – with a mean of .62. Major narrative reviews of the extensive literature of the efficacy and effectiveness of behavioural and cognitive behavioural therapies with a wide range of adult (and child and adolescent) disorders are consistent with the findings of this review of 16 meta-analyses (Emmelkamp, 2004; Hollon & Beck, 2004).

Also of interest are the meta-analyses of the outcome studies on Rational Behaviour Therapy (the first form of CBT), developed by Albert Ellis. These too have demonstrated large effect sizes. In a meta-analysis of 70 rational-behaviour therapy outcome studies, Lyons & Woods (1991) found an effect size of 1.02 (very large).

Clients who spent longer in therapy with experienced therapists had better outcomes.

In a further evolution of CBT into Acceptance and Commitment Therapy (ACT), a meta-analysis of 21 controlled studies involving patients with a range of problems

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including depression, anxiety and personality disorders, psychosis and other problems, obtained a weighted effect size after treatment and at five months follow-up of .66. (Hayes et al., 2006) Taken together then, the results of these meta-analyses and reviews of meta-analyses – alongside the conclusions of process-change research - do offer strong support for the effectiveness of psychotherapy in general in treating a wide range of distressing symptoms, and for CBT (including RBT and recently evolved ACT) in particular.

One of the criticisms of the broad meta-analytical approach, however, is that many of the studies contained therein are RCTs involving clients who, given the strict randomisation of groups and diagnoses of standalone issues central to the model, are not actually representative of clients who attend clinics day-in, day-out, nor of the context of real clinical practice [Beutler, 1998; Fonagy, 1999; Persons and Silberschatz, 1998; Roth and Parry, 1997; Seligman, 1995)]. As Roth and Parry (1997) conclude, “RCTs are an imperfect tool; almost certainly their results are best seen as one part of a research cycle.”(p.370).

In summary then, commentators agree that the efficacy and effectiveness of psychotherapy has been demonstrated. Key illustrative meta-analytic studies and reviews quote effect sizes ranging from .62 to 1.27 indicating moderate to large changes attributed to post-treatment outcome. Most of these, however, are based upon RCTs, which make up only one part of the research cycle. For a researcher interested in pragmatic, clinically representative effectiveness, such as this one, this is where naturalistic studies come in.

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Literature Review: Naturalistic and Effectiveness Studies

Seligman (1995) most notably discriminated between efficacy – which is now used to denote the effects of psychotherapy as determined by RCTs conducted in laboratorylike conditions, and effectiveness – which describes the effects of psychotherapy as measured in clinical practice.

The latter is the other central part of Roth & Parry’s (1997) research cycle: research conducted within real clinical settings. In contrast to most RCTs conducted in laboratory-like conditions, naturalistic outcome studies are carried out under the conditions of clinical practice. Patients with co-morbidity, as usually occurs in clinical practice, are treated (Fonagy, 1999); therapists apply the methods of psychotherapy they usually do and are experienced in; patients themselves make the decision to opt for a particular type of psychotherapy and psychotherapist; and the duration of treatment is determined by clinical requirements (Seligman, 1995). As such, naturalistic study findings are judged to be more representative of standard behaviour in relevant populations with a high degree of clinical representativeness (Staines, 2007; Shadish et al, 2000).

The pendulum swing from efficacy to effectiveness across the decades has generated various dichotomies. One was the assertion that controlled studies secured internal validity while naturalistic studies secured external validity, with neither offering the opposite. Such concerns can be assuaged if looked at on a philosophical level through the prism of structuralist theory, rather than the outmoded statement theory (Westmeyer, 1982). In the latter, it was assumed that theory had a universal validity or application, whereas within structuralist theory, a domain of intended applications is regarded as an integral component of the theory and the hypotheses derived from it (Westmeyer, 1982, 1989). From a structuralist perspective, there are no context-free hypotheses – they always refer to specific contexts or intended applications. Therein lies their internal validity. Just because psychotherapeutic methods work in laboratory conditions, it does not necessarily mean that they will work equally well in the field.

‘For this purpose’, as Leichsenring and Rabung, (2004) argue, ‘naturalistic studies are required’ (P.142).

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In general, three methods have been used to study the effectiveness of psychotherapy in natural settings: clinical representativeness, direct comparison, and benchmarking (Minami et al., 2008).

First, clinical representativeness studies statistically estimate effectiveness from efficacy studies. Most comprehensive clinical representativeness studies are metaanalyses conducted by Shadish and colleagues (Shadish, Matt, Navarro, & Phillips, 2000; Shadish et al., 1997). They estimated effectiveness of psychotherapy from an original pool of approximately 1,000 independent clinical trials. While they concluded that the benefits of psychotherapy provided in clinically representative environments are similar to those attained in treatments delivered in clinical trials, caution is advised in interpreting their conclusions (Minami et al., 2008). Only about 5% of the treatments met minimal criteria for clinical representativeness (Shadish et al., 1997). Moreover, the estimates of effectiveness within them are based upon statistical estimations rather than actual clinical outcomes in natural settings (Minami et al., 2008).

The second means of testing effectiveness involved the translation of ESTs (Empirically-Supported-Treatments, determined so by the findings of RCTs) into natural settings with pre- and post-treatment scores compared to Treatment As Usual (TAUs) in those settings. A multiplicity of studies have examined the feasibility of these ESTs or manualised treatments in natural settings for various disorders (e.g., Addis et al., 2004, for panic disorder; Merrill, Tolbert, & Wade, 2003, for depression; Morgenstern, Blanchard, Morgan, Labouvie, & Hayaki, 2001 for substance abuse).

These have yielded mixed results: The CBT versus TAU applied in a community setting for substance abuse resulted in comparable outcomes (Morgenstern et al., 2001) whereas the EST for panic control therapy applied to a managed care environment generated better outcomes than the TAU (Addis et al., 2004).

Various moderators of such outcomes have been postulated. These include the fact that often the therapists in the TAU condition don’t receive additional training about the condition being treated (e.g. Addis et al., 2004). Significant differences in dose of treatment between ESTs and TAUs have been found in others (e.g. Verheul et al., 2003). Allegiance effects have also been pointed to as favouring the EST as it is often therapists delivering that arm who are part of the experimental arrangement (e.g.,

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Addis et al., 2004). Indeed after conducting a meta-analysis of the youth literature, Weisz, Jensen-Doss, and Hawley, (2006), argued that no study adequately controlled for confounding variables such as setting, therapist training, and dose of treatment.

They urged caution and diluted confidence in the possibility of such treatment methodology producing unbiased estimates of TAU effectiveness.

A promising method according to Minami, Serlin, Wampold, Kircher, & Brown (2008) and others (Wade et al., 1998; Merrill et al., 2003; Weersing & Weisz, 2002) for evaluating psychotherapeutic effectiveness without altering any aspect of TAUs or obviating comparison to a treatment delivered with favourable conditions (therapist training etc.), is to use benchmarks created from clinical trials. Specifically, these benchmarks would allow pre- and post-treatment data in natural settings to be compared to pre- and post-treatment data from clinical trials. Wade et al., (1998) succinctly summarise benchmarking as: “In essence, we use the magnitude of change obtained in efficacy studies as a benchmark against which to judge the magnitude of change in service clinical settings” (p.231). As such, benchmarking is a way to assess the absolute magnitude of effect size relative to a dataset larger than that within the narrow parameters of a single study; it’s an external comparison group, if you like.

In their assessment of the outcome of individual and group CBT in a Community Mental Health Centre (CMHC), as benchmarked against the findings of two clinical trials (Barlow et al, 1989; and Telch et al., 1993) Wade et al., (1998) concluded that their treatment outcomes and the benchmark outcomes were similar. However, they didn’t go so far as to statistically compare their data with the benchmarks’. In a similar design and identical methodology in the same CMHC setting, Merrill et al., (2003) concluded that the effectiveness of CBT provided to clients with major depression compared favourably with the clinical trials benchmark.

By constructing benchmarks from meta-analysis (of 13 CBT clinical trials) rather than selecting a number of clinical trials, as had been the case previously, Weersing & Weisz (2002) significantly improved on the methodology of Wade et al.’s (1998) study. They created a ‘best practice benchmark from a review of the entire youth depression treatment literature’ (p.300) according to Minami et al., (2009). When they compared the symptom trajectory of depressed youths provided with TAUs in

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community mental health centres to that benchmark, the positive results they obtained were all the more robust for it.

Further building on Weersing & Weisz’s (2002) methodology, Minami, Serlin, Wampold et al., (2008) proposed a statistical analysis technique to investigate effectiveness and conducted a benchmarking study on (TAU) psychotherapy treatment outcome for adult depression provided in a managed care environment (HMO). Specifically, data on adult patients diagnosed with major depressive disorder (APA, 1994) were statistically compared with benchmarks derived from clinical trials for adult major depression treatment. The benchmarking was conducted with samples that were progressively matched to the clinical population most commonly observed in clinical trials on the basis of their inclusion-exclusion criteria and medication use.

They found that TAU was indeed effective, generating an effect size similar in magnitude to those observed in clinical trials, based on a comparison calculation of within 10% effect size. It is one that subsequent research has been able to develop and exploit.

Minami et al., (2009) did just that when they explored the effectiveness of psychological treatments delivered at a University Counselling Centre. In it, the outcomes of individual and group CBT treatment of major depression were compared to those of a clinical trial’s benchmark, and found to be comparable.

However, the question needs to be asked as to whether comparisons of naturalistic studies against clinical trials, regardless of their internal validity or reliability of findings, are not inevitably dogged by the fact that naturalistic and clinical trials differ drastically from each other? Are the issues, as previously discussed in this paper, of random assignment, fixed number of sessions, differential training, and exclusion of co-morbidity not ultimately undermining (Minami & Wampold, 1988; Minami, Wampold, et al., 2008; Nathan, Stuart, & Dolan, 2000; Wampold, 1997, 2001; Westen, Novotny, & Thompson-Brenner, 2004)?

In order to address a potential mismatch in benchmark from one to the other, certain precautions are advised (Minami et al., 2009): calculating benchmarks on the back of meta-analyses of a number of clinical trials is more rigorous, as per Weersing &

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Weisz (2002). Ideally, the benchmark and outcome measures used to assess effectiveness should be identical. In cases where they aren’t, the measures should be matched on both reactivity [based on who reported the symptoms: from clinician (high) – client (low)], and specificity [whether the measure reflected clinical symptoms of a specific diagnosis (high) or general clinical symptoms (low), (Minami et al., 2009)]. Meta-analyses show that measures that are higher in reactivity and specificity result in larger effect sizes (Lambert et al., 1986; Minami et al, 2007; Smith et al., 1980). Moreover, it was concluded, following Minami, Wampold, et al., (2008), that within 10% of the benchmarks was considered to be clinically comparable.

There had and have been reservations that treatment in natural clinic settings was effective at all (e.g., Bickman, 2002, Stone et al., 2000, Weeersing & Weisz, 2002, Weisz, Jensen-Doss, & Hawley, 2006), and so benchmarking has proven to be a viable way ‘to demonstrate the effectiveness of our clinical services’ (Minami et al., 2008, p.310). However, like all methodologies, these benchmarks, as Minami et al., concede are ‘nowhere near perfect’. What methodology is? But still, they conclude, benchmarks are ‘the best we currently have’. (Minami et al., 2008, p.319).

In summary then, central to the possibility of building meaningful ecologically-valid research in naturalistic settings, is the development of benchmark methodology in which pre- and post- treatment measures in naturalistic studies can be statistically compared to those of clinical trials to determine acceptable effect sizes to understand what constitutes an acceptable definition of ‘effectiveness’. Key to the reliable conducting of such studies is the matching of benchmarks in terms of conditions measured and outcome measures used.

The benchmarking methodology has since been employed in numerous studies (e.g., Clark et al., 2009; Andrews et al., 2011; Andrews et al., 2013; Andrews et al, 2015) facilitating the measurement of treatment effectiveness and also the development of Practice-Oriented Research, or Practice-Based Evidence, which allows the individual clinician to assess his/her own effectiveness and supports comparisons to national, international and best practice datasets (See Castonguay, 2013 for a full review), which will be explored in the next review section.

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Literature Review: Practice-Outcome Research, Outcome Measurement & Practice-Research Networks In the 2000s, there is an emerging literature about the complementary value of practice-based evidence (e.g., Assay and Lambert, 2004; Barkham and Mellor-Clark, 2003; Duncan et al, 2004a; Evans et al, 2003; Hansen and Lambert, 2003; Lambert et al, 2001; Barkham & Mellor-Clark, 2003). This research is figurative in enabling us to consider the definition of Eysenck’s (1952) ‘independent variable’ as being both intervention and relationship and helping to forward the momentum of psychological research from the Eysenck-Strupp duality (Miller et al., 2013; Castonguay, 2013; Barlow et al., 2013; Wampold, 2015).

Described as ‘a novel approach to overcoming barriers to translating psychotherapy research to clinical practice’ (Tasca et al., 2014) it involves conducting psychotherapy research in applied community and clinical settings, in which clinicians can inform researchers of areas important to them and to their clients (Beutler et al., 1995). This helps to diminish any concerns about the low generalizability of psychotherapy trials (Westen et al., 2004) and ensures research questions explored are of direct clinical relevance (Borkovec et al., 2001) thus helping to heal the practice-research divide many commentators have noted (Boisvert & Faust, 2006; Wilson, Armoutliev, Yakunina & Werth, 2009).

Routine Outcome Monitoring/ Progress Monitoring A ‘cornerstone of practice-oriented research’ (Boswell, 2015, p. 38) is routine outcome monitoring (ROM) and feedback (Boswell, McAleavey, Castonguay, Hayes, & Locke, 2012) also known as Progress Monitoring (PM). Using ROM/PM measures gives clinicians a realistic way to bring research into practice by providing individualised client data to inform therapists of client progress (Ionita et al., 2016).

ROM/PM measures are client-rated measures that provide continuous assessment of client change and provide systematic feedback about client response to treatment (e.g., Overington & Ionita, 2012). With the increasing call for evidence-based practice (e.g., American Psychological Association Presidential Task Force on EvidenceBased Practice, 2006; CPA, 2005; NICE, 2004, 2005a, 2005b, 2006; Mental Health

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Reform, 2016; Dozois et al., 2014), there is an increasing appetite for integrating research into practice, which outcome monitoring facilitates.

The use of outcome measurement is also a response to the fact that deterioration rates in psychotherapy are estimated to range from 5-10 per cent (Lambert & Ogles, 2004) to 80% (Mohr, 1995). And research suggests that clinicians are not reliable in predicting client response, particularly if negative (e.g., Hannan et al, 2005) or identifying those who are not progressing (e.g., Hatfield, McCullough, Frantz, & Kriefer, 2010). These findings are particularly sobering given that therapists usually rely on their clinical judgment to monitor clients’ response to treatment (Gard, 2003; Stewart & Chambless, 2010). Outcome Measurement is thereby a method to help clinicians identify clients who are not responding or deteriorating (see Duncan, 2012; Fitzpatrick, 2012; Halstead, Youn, & Arnijo, 2013; McAleavey, Nordberg, Kraus, & Castonguay, 2012) Outcome measures commonly employed include the Partners for Change Outcome Measurement System (PCOMS) which includes the Outcome Rating scale (ORS; Miller, Duncan, Sorrell, & Brown, 2005) and the Sessional Outcome Scale (SOS; Duncan, 2012; Duncan & Sparks, 2010) the Outcome Questionnaire-45 (OQ- 45; Lambert et al., 1996) the Clinical Outcome in Routine Evaluation-Outcome Measure (CORE-OM; Barkham et al., 2001) and the Treatment Outcome Package (TOP; Kraus, Seligman, Jordan, & Behavioral Health Laboratories, Inc., 2005) to name but four. All of these measures have been subject to rigorous research and refinement (see individual references above for details).

Research has shown that PM measures can be used to monitor clinically significant change in clients (e.g., Lunnen & Ogles, 2005) so that clients who are progressing as expected in sessions can be identified (Finch et al., 2001). The measures can also be used to predict client deterioration, sometimes as early as the third session (Hannan et al., 2005). Identifying deterioration early can enable practitioners to change direction or methodology, thus helping to get clients back on track (Lambert & Vermeersch, 2008).

Indeed, two continuous monitoring and feedback interventions have demonstrated large gains in RCTs and are included in the Substance Abuse and Mental Health

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Administration’s National Registry of Evidence based Programs and Practices (NREPP). Lambert et al.’s (1996) ‘Outcome Questionnaire’ (OQ) system has demonstrated significant gains over TAU in six RCTs with clients at risk for negative outcome or drop-out (Harmon et al., 2007; Hawkins, Lambert, Vermeersch, Slade, & Tuttle, 2004; Lambert et al., 2001, 2002; Slade, Lambert, Harmon, Smart, & Bailey, 2008; Whipple et al., 2003). A meta-analytic review of six studies (n=6,151) using the OQ system showed that clients in a feedback condition had less than half the odds of experiencing deterioration and approximately 2.6 times higher odds of reliably improving, than did those in a TAU condition (Shimokawa, Lambert & Smart, 2010).

Similarly, the ‘Partners for Change Outcome Measurement System’ (PCOMS; Duncan, 2012, 2014; Duncan & Sparks, 2010) – also endorsed by NREPP - has demonstrated significant treatment gains for feedback over TAU in three RCTs (Anker, Duncan & Sparks, 2009; Reese, Norsworthy & Rowlands, 2009; Reese, Toland, Slone, & Norsworthy, 2010). Moreover, the effect sizes reported by studies examining feedback versus TAU have typically exceeded the effect sizes of other widely advocated evidence-based practices (Lambert, 2013).

PM measures are used to compare the data of different jurisdictions, organisations, practitioners and clients (i.e., benchmarking). Benchmarking studies, Wolfe (2012) argues, serve to build the bridge between research and practice by supplying the methodology to build and maintain high standards of psychotherapy (Reese et al., 2014).

Practice Research Networks A growing body of research conducted within practice-based networks (PRNs) – see Castonguay et al., (2013) for a full review - explores the effectiveness of psychotherapies delivered in routine clinical care (Borkovec et al., 2001; Clark et al., 2009; Andrews et al, 2011; Andrews et al., 2013; Andrews et al., 2015) as well as facilitating insight into what clients find helpful and hindering in treatment (Castonguay, Boswell et al., 2010) and predicting treatment outcomes (Ruiz et al., 2004). The PRN model sees the clinicians themselves, or their clients, feeding in data generated by ROM/PM measures that are taken as part of routine clinical practice.

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These in turn are automatically delivered into a shared database in a common practice setting like a university clinic or professional organisation, or for specific disorder (Castonguay et al., 2013; Clark et al., 2009; Andrews et al., 2011; Andrews et al., 2013; Andrews et al., 2015).

The data forms an intrinsic feedback measure to the clinician which can be used to assess the effectiveness of their services and skills relative to other practitioners within one setting or indeed benchmarked against national or group benchmarks (e.g., Brown, Lambert, Jones, & Minami, 2005; Minami et al., 2008; Okiishi et al., 2006; see also, Sales & Alves, 2012; Clark et al., 2009; Andrews et al., 2011; Andrews et al., 2013). In essence, ROM/PM measures ‘provide the means for clinicians to make their practice more evidence-based’ (Ionita & Fitzpatrick, 2014, p. 188). This is entirely in line with APA, CPA, and NICE requirements for continuous client monitoring as part of evidence-based practice (APA, 2005; CPA, 2012, NICE, 2005a, 2005b, 2006).

And so outcome measures used as part of routine clinical care, taken by practitioners become tools for making clinical practice more evidence-based, and are facilitated by the benchmarking methodology identified as central to the development of naturalistic studies and practice-research networks.

Two key studies on Human Givens therapy have used the benchmarking methodology, with one based on data generated exclusively from a PRN to examine its effectiveness. I look at those, alongside other research conducted on the HG approach, next.

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Literature Review: Human Givens Up until 2011, little or no formal research had been conducted into the effectiveness of HG (Corp et al, 2008). In the last five years, however, three key research projects have been conducted: A one-year-long pilot study across a general medical practice in Luton (Andrews et al, 2011) in which it was found to be ‘highly effective’; a yearlong quasi-experimental study set in primary care in the UK (Tsaroucha et al, 2012) in which HG was found to be effective more quickly than a comparison treatment; and, most recently, a five-year long evaluation based on 3,885 subjects, using a practiceresearch-network, in which 71% were appreciably recovered within 4-6 sessions (Andrews et al., 2013). No published research has been conducted in Ireland, to this researcher’s knowledge, although there are approximately thirty Human Givens psychotherapists in private practice here (see www.hgi.org.uk for register).

In the one-year ‘Luton’ study (Andrews et al., 2011) conducted in primary care, data was gathered from a GP practice of referred clients seeking therapy from three Human Givens therapists in private practice. In the five-year evaluation study (Andrews et al., 2013) outcome data was gathered via a practice-research-network; the HGIPRN. This PRN consists of qualified HGI practitioners who apply the HG principles within their practice – some in the public sector, others in the private sector across varying populations and in response to different states of distress. Both studies used CORE-OM and CORE-10 as clinical measurement tools. The latter study concluded that there was psychometric equivalence between CORE-OM and CORE10 (Andrews et al., 2013) and so it will be used in this study (see Methods).

There were limitations to the studies. There was a lack of experimental controls with neither having a wait-list control or established treatment arm. As such, a fully satisfactory causal inference could not be drawn. And indeed, natural recovery without treatment, which is also recognised as a strong possibility where onset has been recent, (Clark et al., 2009) couldn’t be ruled out for similar reasons. Both studies also lack evidence regarding treatment delivery and whether the correct treatment was given, with a call in the 2013 study for more information about the problems treated and outcome for clients to be documented in future studies (Andrews et al., 2013).

Overall, the size of their samples (n=242 in Luton; n=3885 in 2013), and most

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importantly, that their findings were benchmarked against those of Clark et al., (2009) in calculating treatment effect size, a benchmark that is accepted by the research community, are persuasive. Andrews et al., (2013) is an example of a practiceresearch-network building a base of evidence for the ongoing assessment of clinical outcomes. As such it is a pragmatic and valuable addition to a body of research, as Minami et al., argued, (2007), which alongside controlled efficacy studies – for example, RCTs – can offer us much insight and reliable illustration (Roth and Parry, 1997).

Moreover, the data generated by the HGIPRN has since been used to explore another hugely interesting research question around therapist variability and whether ‘deliberate practice’ as a concept could be one applied to high psychotherapist effectiveness (see Chow et al., 2015) as it has been applied to other areas of expertise.

Examples include high performance in sports (Cote, Ericsson, & Law, 2005) business (Sonnentag & Kleine, 2000) medicine and surgery (Norman, Eva, Brooks, & Hamstra, 2006) and music (Ericsson et al., 1993; Krampe & Ericsson, 1996). This demonstrates what I call the long-tail effect in practice-research-networks: the building up of data from which many different research questions can be explored and examined.

The study conducted by Tsaroucha et al., (2012) is a quasi-experimental study of the effectiveness of Human Givens therapy in the treatment of mild to moderate depression in primary care. The research design included a control group in which clients received an alternative treatment. The allocations of subjects to groups couldn’t be randomised, however, for ethical reasons. The outcome testing at four intervals, over the course of a year, was a helpful measure of recovery maintenance with improvements made in both groups following therapy maintained at all time points. In overall terms, HG was seen to be more effective than the control treatment with clients recovering within one to two sessions as compared with four to six for the control group. This study again used CORE-OM as a measure, as well as other tools SWLS and HG Emotional Needs Audit. The latter was verified as reliable in their supplementary study (Tsaroucha et al., 2012b).

The only other outcome research conducted on HG, to this researcher’s knowledge, is informal. The impact of the rewind technique for treating phobias and traumas has

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been looked at (Guy and Guy, 2003; Bishop and O’Callaghan, 2010, for example).

The former was not published in a peer-reviewed journal however, and the latter was a presentation at a British Psychological Society Annual Conference, and therefore, is not considered further here.

Literature Review: Summary Setting out to examine the effectiveness of a relatively new approach without a large bank of research behind it became a quest for this relatively new HG/CBT psychotherapist to make a meaningful contribution to the canon. What approach, as guided by an understanding of relevant literature, would achieve that best?

While an RCT has the ‘gold standard’ badge of sacred research tool upon it (Seligman, 1995, p. 966; see also Chambless & Hollon, 1998; Goldfried & Wolfe, 1998; Kraemer, Wilson, Fairburn, & Agras, 2002) implementing it properly within the context of a Masters programme was not realistic, either in terms of cost or time (see Timulak, 2008).

The newer methodology of effectiveness research, particularly that of benchmarking, offered the opportunity to examine outcome data against much greater datasets than could be generated by a single practitioner in a small three-psychotherapist group practice. (Minami et al., 2007; Andrews et al., 2011; Andrews et al., 2013; Andrews et al., 2015). The key work of Minami et al., (2007; 2008; 2009) and researchers thereafter (Andrews et al; 2011, 2013, 2015) who have carved out a reliable method of benchmark calculation and effect comparison has opened the way to this being rigorous and, thereby, possible.

Two key research studies on the effectiveness of Human Givens therapy had employed benchmark methodology within naturalistic studies and identified a reliable and validated outcome measure (CORE-10) sufficiently brief to be employed in routine clinical care. It thus became a potential benchmark for this study, looking at the same treatment for an expected similar range of problems (anxiety and depression most prevalent in primary care in Ireland, McDaid, 2013), across the same number of psychotherapists.

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The reality that no research on HG had emanated from Ireland, and that we often rely on international studies to assess our effectiveness (see Carr, 2007) meant that a fundamental piece exploring the effectiveness of the approach was pertinent.

Moreover, the findings of the most recent outcome measurement research in its identification of the measures themselves as potential moderators of change (Duncan, 2012; Fitzpatrick, 2012; Halstead, Youn, & Arnijo, 2013) of their use in pre-empting client deterioration and/or drop-out (e.g., Lambert & Vermeersch, 2008) and the potential of being part of a large database which would contribute to the growth of knowledge and understanding of HG in the form of the HGIPRN, as per Andrews et al., (2013) were persuasive. The most recent research on outcome measurement (Miller et al., 2013; Duncan et al., 2015) pointing to the next state of evolution of literature and the possibility of encouraging expertise through the cultivation of ‘deliberate practice’, was also hugely attractive to a clinician embarking on a psychotherapy career, intent on doing as much good as possible, as well as seeking to ‘do no harm.’

And lastly, that now is the time when evidence is called for. Evidence of therapy effect, of progress, of plausibility at an individual (QQI****** with regulation ‘imminent’); at a group level (in public service, see HSE, A Vision for Change (2014); nationally (call for EBP, for e.g., by NICE, APA, CPA); and internationally (see WHO, 2014) suggests that this endeavour was an accessible and worthy one to conduct within routine clinical practice.

For all of these reasons, the decision was taken to assess the effectiveness of HG therapy for the treatment of distress in routine psychotherapeutic practice in Ireland, replicating, and benchmarking against, Andrews et al., (2011) and comparing to Andrews et al., (2013).

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Methods

Methodological Issues of Studies Reviewed In summary, the review of literature in the area led to a number of insights:

• In the 2000s, Practice-based evidence generated a great wave of research demonstrating its credible and valued role in the research cycle (Roth and Parry, 1997; Evans et al, 2003; Miller et al, 2004).

• Naturalistic studies are clinically representative (Shadish et al, 2000).

• The internal validity of naturalistic studies can be secured via contextualised hypotheses as understood from a structuralist perspective (Leichsenring and Rabung, 2004; Westmeyer, 1982, 1989).

• No published research has been conducted on HG therapy in Ireland.

• Existing research on HG is Practice-based evidence from naturalistic studies.

• The 2011 study by Andrews et al., could be used as a benchmark against which the effect size could be established, as it had benchmarked against Clark et al., (2009). The expected problems as usually manifest in primary care in Ireland - depression and anxiety - were matched, the treatment was matched, and referral process was in part matched. It therefore made for an appropriate benchmark (Minami et al., 2009).

• CORE-OM and CORE-10 are the common tools used across all three main studies assessing the effectiveness of HG.

• Andrews et al., (2013) demonstrated psychometric equivalence between CORE-OM and CORE-10, with the latter’s brevity a key advantage for ease of use within regular clinical practice.

• Previous HG studies were weakened by a lack of specific information about problems experienced and no information on the client’s experience of therapy.

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Purpose of the Study The purpose of this study was to see if HG treatment of psychological distress in a clinical setting in Ireland was found to be as effective as it has been demonstrated to be in the UK.

For the purposes of this study, and in line with previous research, ‘psychological distress’ is understood to manifest in symptoms of depression, anxiety and high emotional arousal alongside signs of interruption of social and general function. This reflects real world psychotherapy practice, where therapy is often given and received without clinical diagnosis (Staines, 2007).

Research Question Is HG Therapy effective in the treatment of psychological distress in routine clinical private practice in Ireland, as benchmarked against a previous study into HG effectiveness in UK-based populations (Andrews et al., 2011)?

Study Design A naturalistic study set in a private psychotherapy centre to assess if Human Givens therapy was effective in relieving psychological distress in routine clinical private practice in Ireland, through the implementation of pre- and post- CORE-10 outcome measures. This was benchmarked against the one-year study of HG practice in the UK (Andrews et al., 2011).

Hypothesis Human Givens therapy will lead to a reduction in psychological distress in individuals attending a private psychotherapy practice in Ireland, as measured by CORE-10.

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Population, Sample and Sampling As is the norm in naturalistic studies, there was no attempt at randomization of sampling given the context of outcome measurement being conducted in the field (Leichsenring and Rabung, 2004). The sample consisted of clients seeking therapy at a private psychotherapeutic practice in Dublin, Ireland within the data collection periods, who fitted the inclusion criteria, and who consented to participate in the study. All clients who agreed to participate were briefed and asked to sign a consent form (Appendix 1.2) before proceeding.

Exclusions •

clients under 18 years of age. We were looking at adults, and benchmarking against a study looking at adults (Andrews et al., 2011);

•

those who did not want to participate (treatment proceeded regardless);

•

any clients who chose to remove themselves at any point. (None did);

•

any clients who had literacy challenges, who found form-filling off-putting.

(This did not occur).

The population drawn from was clients embarking on therapy at the Centre. The sample size within the time periods was 35 clients. While the sample size was smaller than would have been wished, the calculations of effect size adjusted for the sample size to maximise the potential power available (Dallos and Vetere, 2005). It is however understood that this sample size is a limiting factor in conclusions drawn from the study (Dallos and Vetere, 2005).

Study Setting The study was set in a private Psychotherapy Centre in Dublin, Ireland. It is in a relatively affluent, established suburb in south county Dublin, but psychotherapists also offer reduced-cost therapy to unemployed and low-income earners. Attending clients were a mix of self-referred, GP-referred, and those attracted by word-of-mouth recommendations.

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This particular Centre was chosen for the study as the researcher practices there and thereby, enjoyed ease of access. It is understood that it is preferable to separate the researcher/data collector roles. For the purposes of this study however, and in order to maximise the sample size, the decision was taken for the researcher to take on the dual role. (This of course is customary in practice-based research and practiceresearch-networks (see Castonguay et al., 2013) All three psychotherapists have between 2 and 9 years of experience in treating psychologically distressed clients with the Human Givens approach. All follow the structured Human Givens Approach and are fully trained in it, accredited, supervised and insured.

Data Collection Methods Data was collected pre and post-treatment and fed into a computerised data collection system, ‘Pragmatic Tracker’ (see www.pragmatictracker.com). This is a web-based application to record and analyse change, using measurement with questionnaires. No client identifiable information was used. (See Procedures for further detail).

Clients were identified by a formula derived by the psychotherapist team to ensure no duplication (initials.date.timeoffirstapt). In this way, therapists would be able to readily trace the identity of their clients by cross-referencing with their own client notes, while most importantly, ensuring client confidentiality.

Data for their own clients only were visible to each psychotherapist – excepting the psychotherapist/researcher who could access all for analysis purposes. Practitioner identity was readily apparent to the researcher. This was deemed necessary given that ‘therapist’ can be such an important variable in the experience of therapy, as much research has demonstrated (e.g., Kim, Wampold & Bolt, 2006; Lutz et al., 2007).

A total of 35 clients, who started and finished a course of therapy within the data periods were seen by the three psychotherapists.

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Measures 1) Outcome Measure Clinical outcome measures were collected before and after sessions. The measures were emailed to the client in advance of the session (the day before) or filled in just prior to, or at the beginning of, a session, depending on client preference. In order to be able to benchmark against the most relevant previous study (Andrews et al., 2011), and to maximise clinical utility, the CORE-OM system was used rather than a disorder-specific measure (Connell et al, 2007).

CORE-OM has been demonstrated to correlate with measures generally used to assess general anxiety and depression e.g., Beck Depression Inventory (Beck et al, 1996; Cahill et al, 2006). These problems are the most common problems for which people seek help in primary care in Ireland (McDaid, 2013).

While designed to use CORE-OM, a 34-item questionnaire as its primary pre-post measurement tool, with CORE-10, a 10-point cut-down version to be employed only between sessions, Andrews et al., (2011) ultimately relied upon CORE-10 data to draw most conclusions.

Andrews et al., (2013) reported near identical effect sizes for CORE-10 and CORE-34 [(d =1.41 (CORE-OM) vs d = 1.27 (CORE-10]. The coefficient � for the CORE-OM and CORE-10 were �= 0.93 and � = 0.82, respectively. These findings echoed Connell et al’s., (2007) demonstration of a high correlation between CORE-10 and CORE-OM (r = 0.94 in a clinical sample), and their conclusions that CORE-10 was sensitive to change and its internal consistency was high (� = 0.82).

Ultimately, it was judged that CORE-10’s brevity would optimize data completion in terms of load on clients and therapists so CORE-10 was chosen as the pre- and postoutcome measure as well as sessional measure in this study. (Brevity of questionnaire was strongly argued for also by Clark et al., 2009).

CORE-10, a derivative of the 34-point CORE-OM, is a ten-point questionnaire which assesses subjective well-being. It includes both positively and negatively framed and

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low and high-intensity items assessing symptoms, (anxiety, depression, physical problems, trauma), functioning (general functioning, close relationships, social relationships), and risk (to self and others). It uses a 0-4 Likert-type scale rated ‘over the last week’ measuring response across a range from ‘not at all’ to ‘all of the time’.

Internal consistency (.94) and 1-week test–retest reliability for the measure (Spearman’s .90 [Evans et al., 2002]) are both high (see Appendix 1.3).

The recommended cut-off between non-clinical and clinical populations is 11 (Connell et al., 2007).

2) Clients’ Perception (Qualitative Measure) Given the extensive body of literature on the ‘common factors’ widely recognised for their importance in generating positive therapeutic outcomes - the therapeutic alliance, for example (Bordin, 1979; Horvath and Symonds, 1991) - it was important to get a picture of clients’ experience of therapy.

PRN-F5, a simple questionnaire used to solicit feedback from clients about their experience of therapy, was used as a way to systematically gather information on clients’ perceptions. Based on Wolpe’s (1969) subjective units of distress scale, it posited four questions about their experience of therapy, and invited further open comment (see Appendix 1.4).

PRN F5 questions covered whether: -

the client felt heard and understood

-

the discussion centred on issues that mattered to him/her

-

s/he found the session helpful

-

s/he was happy with the service being received

Measured on a scale of 0-10, with 0 meaning ‘not at all’, ‘not at all happy’ or ‘not at all helpful’ and 10 indicating ‘very much’, ‘very helpful’ or ‘very happy’ respectively, it was emailed to the client after each session at the Centre.

It was sent by the ‘Centre team’ rather than by a client’s individual therapist to

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facilitate honest feedback.

Given the salience of the client’s perception of the therapy alliance (see Chow et al., 2015) and his/her interpretation of the overall effectiveness of treatment for him/her, this wss to qualitatively illustrate and complement the quantitative findings of effectiveness.

Procedures Allocation to Therapists All clients were directly referred to individual therapists; that is, clients asked for specific therapists either on others’ recommendations or by referral. This is customary practice at the Centre, which works as a collection of standalone therapists.

While it is understood that this dilutes the study’s internal validity (Timulak, 2008) random allocation to therapists was not possible within the day-to-day running of the Centre.

For any clients seeking a therapist without prior recommendation, the intention was to alternate referral across all three. Within the data collection period, this did not occur.

Therapists’ Briefing All three psychotherapists at the practice were familiar with the Pragmatic Tracker and how to use it. A briefing was held in advance of data collection where protocols on client IDs were agreed. Support Therapist notes were drawn up outlining the required process and distributed in hard and soft copy (Appendix 1.5). (This was repeated in advance of the second data collection period).

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Client Briefing and Procedures 1.1 At the start of their first appointment – for which clients were asked to arrive early - respective therapists briefed their clients as to the nature of the study and asked if they would like to participate. Those who agreed were asked to sign a consent form.

They were then asked to electronically ‘fill in’ the CORE-10 form on a laptop/ipad, or if uncomfortable with technology, on paper. This took place in the waiting room and not in the therapy room. For those for whom time was an issue, they were sent the questionnaire the day before their session following a briefing by email or phone.

Again they were given the consent form (Appendix 1.2) and background information sheet (Appendix 1.6).

1.2 After each session, clients were electronically sent the CORE-10 and PRN F5 to fill in. Any who didn’t, were asked to fill it in on a laptop/ipad, in the waiting room, when they arrived for their next session, as per 1.1 above. If filled in on paper, they were asked to leave them in the waiting room in a designated box.

1.3 Following the last planned session, clients were asked to fill in CORE-10 and PRN 5 for the last time. If therapy ended in an unplanned way, the measures were emailed to them.

1.4 Within a fortnight of MA dissertation submission, all participants will be emailed a letter thanking them for participating. Letters to those who chose to be notified of the results of the study (an option given on the consent form) will include that information.

Interventions Clients were treated with the Human Givens psychotherapeutic approach by all three psychotherapists. This bio-psycho-social approach involves many different tools and techniques, which can be applied with the intention of helping clients to address, repair, or manage symptoms of distress. (Griffin & Tyrrell, 2007)

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While no exact treatment protocol was agreed beforehand – given the individualised nature of the treatment in application with real and varied clients in a real-world setting - the approach is characterised by three key commonalities across the three therapists, all of whom were trained by the same tutors at the same college (Human Givens College: www.humangivenscollege.com): 1) Problem Conceptualisation All HG-trained therapists understand distress as a manifestation of innate needs going unmet as a result of: a) missing coping skills, whether never learned or forgotten b) trauma (sub-threshold or otherwise) or mis-use of innate resources c) a toxic environment Treatment consists of psycho-education and the application of strategies to help the client to use their resources well, and/or learn skills if a deficit is indicated, to get their needs met and thereby get well as quickly as possible.

That overarching

treatment aim is facilitated by two common methodologies: 2) Session structure. All sessions are structured according to the RIGAAR acronym: R- Rapport – to build connection with a client before doing any active treatment I - Information-gathering – using active listening to gather information about a client’s problem and about his/her resources and strengths G- Goal-setting – all sessions are targeted around identifying key goal(s) which are needs-oriented and will help a client to move forward A - Accessing resources – identifying key resources, which will help the client to reach their goal(s) A - Agreeing strategy – agreeing the strategy that will help the client to reach their goal(s) R - Rehearsal – using the power of rehearsal (in guided imagery) to harness their imaginations to activate the resources to employ the strategy to reach their goal and move toward getting their needs met.

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3) Intervention point The decision as to how to understand patterns of behaviour and decide the key points of intervention is always guided by another acronym, APET: A – Activating trigger. Something in the environment that triggers a memory of past experience which generates: P – Pattern match – a match to a previously learnt pattern of behaviour and associated events and emotions (somewhat akin to ‘schemas’) – that in turn generates E – Emotions – all patterns are imbued inevitably with emotions that trigger before (as per leDoux, 1998, for example): T – Thought – or action. Which is emotionally driven as a result of pattern matches (which can be conscious or unconscious).

HG therapists are solutions and forward-focused, excepting where they see patterns of behaviour in the present as manifestations of past trauma – via APET. In these instances, they intervene to either scramble the ‘pattern’ (similar to ‘schemas’) via the rewind technique or de-sensitisation in guided imagery. This is similar to imagery resculpting (Muss, 1991; see Wheatley & Hackmann, 2011) or they seek to bring down the emotional arousal via relaxation and meditation techniques, in the first instance.

Behaviour activation skills are also used to change responses to triggers, which are rehearsed in guided imagery while the likelihood of the adoption and activation of desired behaviour in the future is increased via resource activation techniques (Gassman & Grawe, 2006), including the use of embedded suggestions, reframing, reminding and emphasising client resources and rehearsing therapeutic change in trance. (See Griffin & Tyrrell, 2008; www.hgi.org.uk).

So, while the approach allows for a great deal of flexibility on the part of the therapist in terms of application of the tools to address their interpretation of their unique client’s circumstances and what will best help, the overarching frames of problem conceptualisation, session structure and intervention point allowed for some consistency in therapy approach.

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Data Analysis Methods To assess the effect of treatment on study subjects, pre-post effect sizes were calculated using pre, post and pooled standard deviations, in order to flexibly compare with previously published studies – depending on which deviations respective studies used (Becker, 1988; Smith et al, 1980).

These were calculated using the formula recommended by Becker (1988), using the pre-treatment standard deviation and correcting for bias in estimation as established

by Hedges (1981). This correction was particularly important given the relatively small sample size.

Benchmarking Findings were benchmarked against Andrews et al.’s, (2011) study to determine whether effect sizes were clinically equivalent to its and previous studies’ findings on effectiveness in real world settings. Andrews et al., (2011) research findings were based on practice-generated data collected by three therapists over a year in primary care in the UK. (Its findings, in turn, were benchmarked against those of Clark et al., (2009), which had demonstrated clinical equivalence).

If the pre-post treatment effect size estimated from the data in this current study was within 10% of the Andrews et al., (2011) data, the effect size would be judged as clinically equivalent to that of the benchmark.

The degree of clinical change, reliable change and recovery were calculated based on criteria illustrated in Connell & Barkham, (2007).

Change was determined to be ‘reliable change’ if clients experienced a reduction in their CORE-10 score of six points or more. ‘Recovery’ was signified by movement from at or above the cut-off score of 11 to below the cut-off. (See CORE Systems Group, 1998; Jacobson and Truax, 1991; Connell & Barkham, 2007).

First examined was within-subjects effectiveness: the changes in clinical symptoms from pre-treatment to post-treatment.

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Then we looked at any between-subjects effects to examine if moderators like planned/unplanned endings, medication, or gender had impacted significantly on the treatment effect, as had been found in part in Andrews et al., (2011) and Andrews et al., (2013).

Pilot study A pilot study was conducted in February 2015 at the centre of study to assess if there were any elements of the research design that proved to be problematic in practice, which would benefit from refinement in the study proper (Mcleod, 2007). No large issues arose. A decision was taken to email out outcome measures when at all possible as it facilitated efficient time management within busy days.

Issues of reliability and validity Internal validity was difficult to secure given the lack of randomization of sample (a function of naturalised study) or assignment to randomised psychotherapist (a function of the nature of referrals to a co-operative private practice where client choice must take precedence). As such, broad generalizability of findings for the study are diminished. However, the specificity of the hypothesis helps to build in a degree of internal reliability (Leighsenring and Rabung, 2004). In terms of the external validity of the study, in its application of Human Givens therapy in the field, it is solid. It is representative of real-world private practice (Shadish et al, 2000).

While it could have been advantageous to set up a control wait-list group (Elliott and Brown, 2002) this study did not choose to seek control through this method for ethical reasons: Treatment for those experiencing distress should proceed as quickly as possible and not be impeded for the sake of research alone. A conclusion that other researchers have also reached (Tsaroucha, 2012). Indeed research has demonstrated that time spent on a waiting list can adversely effect likelihood of engaging in therapy (e.g., Bell & Newns, 2004; Reitzel et al., 2006). That control was not present in the study against which this is being benchmarked (Andrews et al., 2011).

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Reliability of the findings of the study overall is facilitated by it being benchmarked against a previous 1-year long study (Andrews et al., 2011) – as well as being compared to the extension of that benchmarked study (Andrews et al., 2013). The measures chosen match with those used in previous research, which, again, enhance the reliability of findings.

Ethical Considerations The research design gave due consideration to the moral principles laid down by Beauchamp and Childress (2001) to ensure that this research was conducted ethically and responsibly.

The intention of the researcher was non-maleficent, with study design elements seeking to ensure that no harm was done to those choosing to participate in this study – including no wait-list control – and inviting only clients attending for treatment, to participate. Indeed, the study was designed in such a way as to ensure that treatment evolved as it would naturally, outside of the direct study context. The use of brief outcome measures used in routine practice, underlines the integration of the study measurement element into regular care. In so doing, it ensured that clients’ experience of therapy was not being manipulated for the sake of research – which would have been the case if in a more controlled in RCT context (see Haman & Hollon, 2009).

However, in ethical terms, such a well-designed RCT could have offered greater controls and protection of participants in other ways. Ultimately, the limited time and resources available to complete it informed the choice of research design for this study.

The use of brief outcome measures can also be seen as ethically beneficent, given the research on the benefits outcome measures can make in facilitating quicker outcomes and pre-empting client drop-out (e.g., Harmon et al., 2007; Lambert & Vermeersch, 2008). The impact of their use on therapists too was taken into account, with research demonstrating their benefits for therapist effectiveness (Miller et al., 2013).

Familiarity and ease of use were also secured for ethical as well as practical reasons, with therapists trained on the Pragmatic Tracker system, as well as having appropriate

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training on all elements of the procedure (Duncan et al., 2004b; Lambert et al., 2001).

In overall terms, the intention behind the study was, as described in the introduction, that its findings would be of benefit to participants, to potential clients, and to those in psychological distress in general through the experience of direct, effective therapy and the building of an evidence base to demonstrate the effectiveness of a short-term treatment intervention. The short-term nature of the treatment deserves particular attention. Ethically, it is absolutely in the client’s best interest – and not necessarily in the therapist’s if seen on a financial basis alone, for example – that therapy is as quick and effective as possible. Bringing a treatment demonstrated in previous research to work quicker than others (Tsaroucha et al., 2012a; Andrews et al., 2011; Andrews et al., 2013) to the attention of clients and policy-makers in this country fundamentally correlates with Haman & Hollon’s (2009) conclusion on quintessentially ethical research. They argue that ethical research is that which ultimately ‘leads to the delivery of more efficient and effective care to those who will most benefit.’ (p. 161).

The confidentiality of participants was guaranteed, as described, and the exclusions listed in the methodology section of this proposal were the only ones that pertained.

Consent to participate was secured in writing.

All participating psychotherapists are bound by the ethical, governance and disciplinary procedures of their accreditation body – The Human Givens Institute (see www.hgi.org.uk) and carry appropriate indemnity insurance through their own policies.

The Pragmatic Tracker web-based system has been developed with great attention to the ethical implications of the electronic transfer of data. The system is designed so that all client identifiers are removed and a strict code of practice with respect to usage of the information is adhered to. This means that though the researcher was able access the data, only the individual psychotherapists knew the identities of their own clients. Given the context of this research – being conducted as part of an MA degree - anonymised data can be made available to the markers of the research thesis, if required.

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All paper documentation used by psychotherapists with clients who participated in this evaluation will be retained with the clients’ notes and, in line with guidelines laid down by the Data Protection (Amendment) Act (2003) will be retained for a minimum period of 7 years.

In order to be ethical, research needs to be relevant. The author considers this research question to be timely and highly relevant in light of the increasing call for accountability of our profession (Department of Health (UK), 2004; NICE, 2005; impending regulation for counsellors in Ireland, see Oireachtas Website reference *****).

Finally, Bond (2004) considers that there is an ethical expectation on researchers to actively seek out avenues for dissemination of research findings to all relevant and interested parties. Following submission of the study for the purposes of the Masters degree, the author will seek to have it published. Correct attribution, citation and referencing will be conscientiously demonstrated – another ethical consideration.

The findings will be disseminated through the various channels of connection provided by the researcher to all interested others. The possibility of preparing an article for submission to a peer-reviewed journal will also be pursued.

Ethical approval for this study was sought from the Human Research Ethics Committee overseeing Masters students’ theses at the Irish College of Humanities and Applied Sciences, and was granted on 26 January, 2015.

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Methods Summary While limited time and budget decreed that conducting a more robust RCT to test efficacy was impractical, this naturalistic study based on practice-generated data, nonetheless provided valuable insights into the effectiveness of a short-term psychotherapeutic treatment for psychological distress.

In so doing, it has offered three key benefits: a building, objective evidence base to aid client protection and client confidence; on-going measurement to help direct the path of treatment – and support and catalogue therapist decision-making; and a model of accessible practice research generation for psychotherapists across modalities, not just Human Givens.

At a time when there is a great need for the alleviation of psychological distress, and a demand for evidence-based interventions at a professional and societal level, this study was timely.

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Findings Data was collected from thirty-five clients within two data collection windows February - May, 2015, and February - June, 2016.1 Thirty-five clients in total took part in the study. No clients who were eligible within the data collection periods refused to take part in the study. (See Figure 1 for client enrolment in therapy and outcome flowchart).

All 35 clients returned for at least one additional session and were therefore considered to have ‘entered treatment’.

Of those who entered treatment, all 35 clients had valid pre- CORE-10 measures but two clients failed to deliver valid post-CORE-10 measures. This means that valid post data were obtained from 33 clients (94.29%).

The CORE-10 system uses a clinical cut-off of 11 at pre-treatment. 28 (85%) of the 33 clients who had a valid initial assessment were above the clinical cut-off.

Medication Use At their first session, six clients (18%) were on medication (anti-depressants). Four of these were women (67% of medication-taking sample; 25% of women sampled overall) and two were male (33% of medicine takers; 18% of men participating in the study overall).

1 The decision was taken to extend the MA to a two-year degree and have a second data collection window at the same time the following year as an insufficient sample was generated in the first data collection window due to personal therapist circumstances (family illness).

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Figure 1: Participant Enrolment & Outcome Measurement Flowchart Participants referred for therapy within two data collection windows

(2015) (2016) n = 7 n= 28

n=35

Invalid pre-post measures n=2 (no post measure avail)

Below clinical cutoff on pre-measure n=4 (CORE-10≥11)

Clinical & reliably improved (Improve by ≥6 CORE-10 points) n= 23

Received Initial assessment & accepted into therapy n=35

Had valid pre- and post CORE-10 measures N=33

Clinical cut-off pre- CORE-10≥ 11 n=28

Clinical but not reliably improved (Did not improve by ≥CORE-10) n=5

‘Recovery’ (improved by ≥6 CORE-10 points and finishes below clinical cutoff (10) n= 20

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Duration of presenting problems The three participating therapists filled in details of the presenting problems and the duration over which those problems had been experienced.

Presenting problems included depression, anxiety, relationship issues, bullying, trauma and PTSD. (See Table 1 for the full range). Participating therapists discussed overall categories beforehand – in line with recommendations of the CORE-10 manual (See CORE Systems Group, 1998).

Table 1: Illustration of problem presentation by client (n = 34) based on reported duration of symptoms at initial assessment Notes: n=34 (Missing problem duration data for one subject.)

Presenting Problem

Duration < 6 months

<1 year

>12 mths

3-5 yrs

Addiction Anxiety

3 (8.8%)

3 (8.8%)

Anxiety/ Depression

1 (2.9%)

Total 1 (2.9%)

4 (11.8%)

2 (5.9%)

12 (35.3%)

1 (2.9%)

1 (2.9%)

2 (5.9%)

1(2.9%)

1 (2.9%)

Anxiety/Bullying Anxiety/PTSD or Anxiety w/ subthreshold Trauma Depression

Recurrent/ continuous 1 (2.9%)

1 (2.9%)

3 (8.8%)

1(2.9%)

6 (17.6%)

1(2.9%)

1(2.9%)

2 (5.9%)

4 (11.8%)

PTSD

2 (5.9%)

Relationship problems

2 (5.9%)

2 (5.9%)

2 (5.9%)

Total ->

5 (14.7%)

6 (17.7%)

9 (26.5%)

2 (5.9%) 6 (17.8%)

10 (29.4%)

4 (11.8%)

In overall terms, problem duration is seen to be relatively extensive, with 85% of problems experienced for more than six months and 68% of problems experienced for over a year. 12% were experienced as enduring recurrent/continuous.

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Anxiety is the most prevalent complaint with 35% of clients presenting with it in the first instance, and it being co-morbid with depression and bullying in two cases (6%) and a single case (3%) respectively, while co-morbid with PTSD or sub-threshold trauma in a further six cases (18%). In total then, anxiety was a primary presenting problem for 62% of the sample.

Trauma in the form of full-blown PTSD, or PTSD with anxiety, or sub-threshold with anxiety, was another prevalent problem with 24% of the sample presenting with it.

Relationship problems were presented in six or 18% of the cases sampled.

Depression – co-morbid with anxiety in two cases (6%) and standalone in four cases (12%) is a little lower than its representativeness in the population. (McDaid, 2013)

Clinical Outcomes

Demographics The mean age of the thirty-five clients included in the study was 36.37 years (SD = 13.63, n = 35), with a median of 31 years (range 18 – 68 years). Twenty-four clients were female (67%) and eleven male (33%).

The population was overwhelmingly ‘White Irish’ (83%) with 9% ‘Other White’ and one each ‘White British’ (3%), ‘White & Black Caribbean’ (3%), and ‘White & Asian’ (3%).

The relatively homogenous sample is in line with the overall

homogeneity of the Irish population (86% of the Irish population is described as ‘white Irish’. See CSO, (2012), p.25).

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Within-study effectiveness The population (n=33), with valid pre- and post-treatment measures had an average 3.21 sessions each.

Table 2 shows pre and post-treatment scores for the population with valid pre- and post- CORE-10 data for the current study and pre-post treatment effect sizes calculated using pre, pooled and post standard deviations to facilitate comparison.

Table 2: Pre- and Post- treatment CORE-10 scores for population with valid pre and post scores and effect sizes (Cohenâ&#x20AC;&#x2122;s d) by type of therapy ending and benchmark CORE-10 data from Andrews et al (2011) Pre-treatment Ending

CORE-10

Planned

n

29

M(SD)

16.41

Post-treatment n

29

(5.49)

M(SD)

8.03

d Pre-

Pooled

Post-

SD

SD

SD

1.52

1.73

2.02

2.08*

0.89*

2.36*

1.57

1.81

2.15

1.33

1.39

1.45

1.13

0.91

0.77

1.27

1.23

1.18

(4.14)

Dublin N=33

Unplanned

4

17

4

(2.16) Total

33

16.48

(1.91) 33

(5.57) CORE-10

Planned

80

18.53

12.5

7.76 (4.06)

80

(7.23)

8.79 (6.61)

Luton (Andrews

Unplanned

27

et al, 2011) N=97

22.04

26

(6.24) Total

107

19.41 (7.13)

14.54 (9.05)

106

10.19 (7.65)

*Hedges correction not available for small sample size, marked * â&#x20AC;&#x201C; therefore raw effect size given for all.

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Of the clients for whom we had valid pre- and post- CORE-10 measures (n=33), the mean pre-score was 16.48 and the average post-score was 7.76, meaning that these clients showed an average improvement of 8.73 points (when rounded to two decimal points). This is fractionally (0.49) less than the pre-post average improvement in Andrews et al.’s, (2011) of 9.22 points. However, the mean post score of 7.76 in the current study is much lower than the mean post-treatment score of 10.19 in the benchmark study.

I also looked at the figures comparing the clinical (those over clinical cut-off, i.e. scoring 11 or more at pre-treatment) with valid pre- and post- CORE-10 scores. Table 3 shows these results as compared with the benchmark, Andrews et al., (2011) scores (full non-clinical group as clinical-only scores weren’t accessible in published paper.)

In order to assess the suitability of the data for t tests, normal probability plots were made of the pre-treatment, post-treatment and the subtraction of post- from pretreatment data.

Figure 2 – Normal Probability Plot CORE10 T1 (pre-treatment)

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Figure 3 â&#x20AC;&#x201C; Normal Probability Plot CORE10 T2 (post-treatment)

Figure 4 â&#x20AC;&#x201C; Normal Probability Plot CORE10 (T1-T2)

As can be seen, in all cases there is a linear relationship. While this is more pronounced in figures 2 and 4, the data in figure 3 was considered acceptable for t tests.

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Significance of treatment

Considering the treatment group as a whole a paired samples t test was used to test for significance between pre- and post-treatment means. For the 33 clients (who all had pre- and post-treatment CORE10 scores), there was a significant difference between the pre-treatment mean (M = 7.76, SD = 4.06, SE = 0.71), t = 7.9815, df = 32, p-value = 4.127e-09, p<.05 (p = 0.000000004127) indicating a very significant effect of the treatment.

Effect size of treatment Effect size measures either trace the sizes of associations or the sizes of differences.

Cohen’s d was used here to measure the treatment effect size, following Andrews et al., 2011 (and Andrews et al., 2013, and Clark et al., 2009) for consistency and comparative purposes. SDpre was used as per the customary formula.

In terms of broad advice given in interpretation of Cohen’s d, Cohen himself suggested that d=0.2 be considered a ‘small’ effect size, d=0.5 represents a ‘medium’ effect size and d=0.8 a ‘large’ effect size (Cohen, 1988). However, interpretation of the results must be nuanced and take into account the number of participants as well as other factors (Field, 2013; Seidel et al., 2013). Typically, with an n>30 any value for Cohen’s d greater than 1 is a large effect.

For the participants of the current study, Cohen’s d = (meanT1-meanT2)/SD T1: 8.73/ 5.57 = 1.56 (large effect size)

Between-Subjects Effectiveness Given that it was a key moderator in Andrews et al., (2011 and 2013), we tested to see if the type of ending was a significant moderator of outcome in this study also.

It was unplanned in only four cases (12.12%) of the total valid pre-post treatment cohort (n=33).

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Two of these four cases for one therapist involved clients who had not finished their cycle of treatment and had changed appointment times frequently. The other two unplanned endings were logistically based – holidays interceding within time available for the data collection of the study.

Moderating effects of different groups (medication and gender) An independent t test was used to investigate any difference in treatment between men and women and between those taking psychotropic medication (antidepressants) and no medication. Considering the change scores as a result of treatment, there was no significant difference between the clients on medication (n = 6; M = 8.67, SD = 6.98, SE = 2.85) and those not on medication (n = 27; M = 27, SD = 6.26, SE = 1.20), p (0.97) > 0.5. There was also no significant difference between men (M = 7.09, SD = 5.91, SE = 1.78) and women (M = 9.55, SD = 6.43, SE = 1.37), p (0.29) > 0.5

Benchmark Benchmarking of the pre-post treatment effect size was assessed by using the CORE10 scores and the pre-treatment SD as the deviation to calculate the pre-post treatment effect size, following Andrews et al., (2011) and its benchmark, Clark et al., (2009).

When benchmarked against the effect size found in Andrews et al.’s (2011) study (d = 1.41), the pre-post treatment effect size of this study, based on all clients in the current study with valid scores at both pre- and post-treatment, was much higher at d=1.56. (See Table 2) If an effect size is 10% more than a benchmark study, it is considered to be clinically equivalent (see Minami et al., 2009; Andrews et al., 2011). At 10.63% higher than Luton’s effect size and 29% higher than its asymptotic d (1.27), it was demonstrably more than clinically equivalent.

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Given the small sample size, however, another measure was taken to underpin confidence in the Effect Size reported. Pearson’s correlation coefficient r was chosen as r covers the whole range of relationship strengths from no relationship whatsoever (zero) to a perfect relationship (1 or -1), and thus tells us exactly how large the relationship is between the variables we’ve tested. In this study, based on the pre-post treatment scores of the full population with valid measures (n=33), Pearson’s correlation coefficient (r) = √t2/(t2+df) = √(7.98 x 7.98)/ (7.98 x 7.98)+32 = √63.68/95.68 = √0.66 = 0.8, so r = 0.8. Again, this analysis illustrates a large effect size.

Given the lower mean pre-treatment score of the valid pre- and post-treatment population as compared to Andrews et al., (2011; m=16.41 v 19.41), another analysis was undertaken to compare the clinical population to the benchmark in order to assess relative outcome – see Table 3.

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Table 3 – compares Clinical population (ie with CORE-10 ≥11 at inception) with valid pre- and post measures compared to benchmark valid (but not clinical) pre- and post-treatment scores.

CORE-10

Ending

Pre-treatment

Post-treatment

n

n

M(SD)

M(SD)

d Pre SD

Post SD

Dublin N=28

PooledSD

Planned

24

17.8

24

(4.67) Unplanned

4

17(2.16)

7.42

2.22

2.51

2.36

2.08*

2.36*

2.21*

2.26

2.33

2.29

1.33

1.39

1.45

1.13

0.91

0.77

1.27

1.23

1.18

(4.13) 4

12.5 (1.91)

Total

28

18.1 (4.4)

28

8.14 (4.27)

CORE-10

Planned

80

18.53

80

(7.23)

8.79 (6.61)

Luton (Andrews et

Unplanned

27

al, 2011)

22.04

26

(6.24) Total

107

19.41 (7.13)

14.54 (9.05)

106

10.19 (7.65)

For the clinical population in this study, the mean pre-treatment score was 18.1 and post-treatment score was 8.41. The pre-treatment score is within 1.3 points of the pretreatment score in the benchmark study and the post-score is lower by 2.05 points ie (ie 20% lower).

There was an ‘extremely’ significant statistical difference in the pre-post treatment scores for the clinical population (pre-treatment mean (M=18.07; SD=4.4, SE=0.83) and the post-treatment mean (M=8.14; SD=4.27, SE=0.80), t=8.5699, df=54, pvalue<0.0001) and the effect size was found to be very large d = 2.29 – 80% higher that of the asymptotic d (1.27)

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And for this clinical population, ending type was seen to be a moderating influence [(planned end: n=24; M=10.83, SD=5.9, SE=1.20) or unplanned (n=4; M=4.5, SD=3.32, SE=1.66), t=2.0632, df=26, p-value= 0.0492 (<.05)].

Again, neither gender nor medication proved to be significant moderators: There was no significant difference between clients who were men (M = 8.38, SD = 6.46, SE = 2.28) and women (M = 10.55, SD = 5.89, SE = 1.31), p (0.40) > 0.5. ) or for those clients on medication (n = 6; M = 7.5, SD = 4.28, SE = 1.75) and those not on medication (n = 22; M = 10.32, SD = 6.44, SE = 1.37), p (0.3235) > 0.5.

Clinical population

Figure 5. The mean pre- and post-CORE-10 scores for clinical group planned (n = 24), unplanned (n = 4), all (n=28) (pre-treatment = T1, post-treatment = T2). (Clinical cut-off = 11) 20 18 16 14 12 10

T1

8

T2

6 4 2 0 Unplanned end Planned End

all

Reliable Change Then the change scores of the clinical population â&#x20AC;&#x201C; those whose pre-treatment score was over the clinical cut-off â&#x20AC;&#x201C; ie 11 or more (Connell & Barkham, 2007) were looked

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at to understand the significance of change in terms of nature of improvement and/or recovery.

Of this population, (n=28), twenty-three (or 82%) reliably improved. This means that they moved by 6 points or more from pre- to post- treatment, over an average 2.96 sessions.

. Reliable Change and Recovery rates for this study ‘Dublin’ v benchmark, Andrews et al., 2011 ‘Luton’ study showing pre-and post treatment effectiveness for clients (n=28) who were clinical at pre-treatment and made full recovery or reliably improved.

Recovery

Dublin n=28

Luton n=97

Measure

Reliable Change

CORE-10

No

3 (10.7%)

2 (7.14%)

5 (17.9%)

Yes

3 (10.7%)

20 (71.4%)

23 (82.1%)

Total

6 (21.4%)

22 (78.6%)

28 (100%)

No

22 (22.7%)

3 (3.1%)

25 (25.8%)

Yes

19 (16.5%)

53 (54.6%)

72 (74.2%)

Total

41 (42.3%)

56 (57.8%)

97 (100%)

CORE-10

No

Total

Yes

Recovery Then the change scores of the ‘recovery’ group (n=20), which is the number of clients who not only reliably improved (i.e. moved by 6 points or more on CORE-10) but who also finished below the clinical cut-off (ie. at 10 or less) on CORE-10 (see Table 4) were examined. Twenty clients were in this group. This means that 71% of the participants with pre-therapy scores in the clinical range (n=28) demonstrated reliable change and also recovered.

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So, 20 (71%) clients from the clinical population have recovered, moving from an average score of 18.45 to 6.25 in 3.25 sessions on average (so full recovery took 0.29 sessions more on average than that for reliable clinical improvement alone).

Client Satisfaction

Of the population accepted into treatment (n=33), the average satisfaction score on PRN F5 after their first treatment, available from 27 clients (82%), was 37.26 (out of a possible score of 40; 93%) and the average score after their last treatment was 37.8 (95%). These are very high scores indicating a high satisfaction with the treatment received.

Of the full Recovery sample (n = 20), session 1 PRN F5 scores were available from 15 clients (75%). The mean score was 38.87 which is a very high score reflecting clientsâ&#x20AC;&#x2122; satisfaction with treatment (97%, n = 15). End of treatment scores were similarly high, with a mean of 38 (95%) across the available fourteen scores.

In overall terms, it illustrates a high level of satisfaction with the treatment at both entry and exit, with only four unplanned endings (12%) as compared to 25% in the Luton study (Andrews et al., 2011) and 27% in the five-year study (Andrews et al., 2013).

The relationship between satisfaction scores at the first session and the average number of sessions attended across the clinical sample were also assessed to see if there was a significant relationship between them, small sample notwithstanding. The mean session score awarded was 37 and mean number of appointments attended was 2.86.

Then the mean session score of the clinical group who gave a satisfaction score at the first session was compared to those who didnâ&#x20AC;&#x2122;t supply one. There was very little difference between those who had given a satisfaction score at the end of session 1

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(n=22; M=2.86, SD=1.39, SE=0.3) and those who didn’t (n=6, M=3.33, SD=1.97, SE=0.8), p(0.5076)>0.5 – therefore the difference was not statistically significant.

Therapist as a Moderator of Change While the small sample size was likely insufficient to generate the power to read statistical significance, given that the therapist can be a key moderator of change, we looked at any differences between outcome by therapists.

One therapist delivered 2 clients, one 11 clients and one 20 clients within the data collection period. The therapist sample with just two clients was insufficient in size to make comparisons against so we looked at clinical group results (given salience of clinical figure in therapist research) for the other two therapists to see if the therapist was a moderator of success.

An independent t test was used to investigate any difference in treatment between clients for ‘Therapist 1’ and those of ‘Therapist 2’. Considering the change scores as a result of treatment, in the clinical cohort for Therapist 1 (n=19; M=11.32, SD=6.36, SE= 1.45) and Therapist 2 (n=7; M=7.86, SD=2.61, SE =0.98), p(0.1795)>0.5, and so there was no significant difference in the outcome for those attending one or the other.

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Discussion Introduction In order to understand the implications of the findings of the current study, I explore its results in relation to research on pertinent methodological aspects – particularly the calculation of Effect Size and those pertaining to the use of Subjective Well-Being measures (SWBs). This was in order to understand the overall pre- and posttreatment size results, in relation to its benchmark, and to the existing deep and broad literature on effectiveness and efficacy.

I then go on to look at potential moderators of the change reported across a number of areas, relating the findings of the current study to that of the benchmark and other relevant effectiveness, efficacy and outcome literature. These include: the type of therapy ending, problem duration, problem type, and problem severity, dose-effect size, client satisfaction - illustrating the potential role of common factors, the therapist as a potential driver of change, outcome measurement itself and the interventions themselves being potential moderators. Under each heading, I explore relevant literature before concluding what this study’s findings demonstrate or suggest, and/or where study design has been insufficient, where changes to the design or an alternative design could potentially deliver further insight.

Results Overview Relative to Benchmark This naturalistic study set in a private psychotherapeutic practice in Ireland set out to replicate the findings in Andrews et al.’s (2011) research, which examined the effectiveness of Human Givens Therapy for the treatment of distressed clients in primary care in Luton, UK. This was achieved by conducting a study of pre-post treatment effect using the same measure as in Andrews et al., (2011) – CORE-10 – with the results benchmarked against Andrews et al.’s larger data sample.

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In terms of demographics, the gender balance in this study (67% female: 33% male) is in line with previous studies (Andrews et al., 2011; 71% female: 29% male, and Andrews et al., 2013; 66% female: 34% male). The homogeneity of the sample (the population was overwhelmingly ‘White Irish’ (83%) with 9% ‘Other White’ and one each ‘White British’ (3%), ‘White & Black Caribbean’ (3%), and ‘White & Asian’ (3%) is also in line with the Luton study (Andrews et al., 2011) in that 86% of that sample was described as ‘White British’, with single ‘Black/Black British African’; ‘White Irish’, ‘White (English/European)’ clients and two ‘Asian/Asian British Indian’. Similarly, ‘White British’ made up a large percentage of the 2013 study, with 75% clients described as such. And so all populations were similarly homogenous which facilitates comparisons being made.

Effect size clinical equivalence was achieved against the benchmark. Indeed the effect size (d = 1.56) was 29% above asymptotic requirement (d = 1.27) for the population with valid pre and post-treatment scores, and for those within the clinical group only, (d = 2.29), 80% above the asymptotic d, demonstrating not just clinical equivalence but better clinical outcome in this study than in its benchmark.

The study also reports that 82% of the clinical population demonstrated ‘reliable improvement’ over an average of 3 sessions, with change scores: (n=25; M=12, SD=5.29). Important to note here is that of the five clients who didn’t reliably improve, three had low scores to begin with: (12, 13, 14). And each of these three improved by 4 or 5 points. Only one of the five showed no change and one disimproved by two points. Such a tiny shift wouldn’t be considered clinically significant, however (see Connell & Barkham, 2007). Moreover, these results are very strong when compared to Andrews et al., (2011) and (2013) in which 74% and 71% respectively reliably improved.

71% of the clinical group recovered and reliably improved (n = 20; M= 12.2, SD=5.21). This is the largest percentage of recovery yet as compared to previous studies on the effectiveness of Human Givens using similar measures; [54.6% recovery was reported by Andrews et al., (2011) and 50.6% recovery by Andrews et al., (2013)].

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Moreover, when session number is compared, this study also produces relatively stronger results: All clients with valid scores in this study, received an average 3.21 sessions. By comparison, in the 5-year study, an extension and replication of Luton based on a larger data set (see Andrews et al., 2013), all clients had 4-5 sessions on average (M=4.69; SD=3.74). Moreover, the cohort with a planned ending – a moderating influence on outcome in the same study, had 5 sessions on average (M=5.09, SD=3.74) in comparison to this study’s 3.04 average number of sessions (M=3.04, SD=1.6).

So what accounts for these strong outcomes and are they as valid and impressive as they first appear?

Methodological Considerations Given the debate that prevails in effectiveness studies around methodologies employed in calculating and interpreting effect size, that makes for a good starting point in assessing the study’s results. As Seidel et al., (2013) note, the variety of methods available for measuring Effect Size (ES) present the clinical-researcher ‘with a bewildering array of options, any of which may significantly alter the reported effect’. (p.12). Some differences amongst the nine ES formulas examined in their study, were ‘as large as the total clinical change commonly attained by clinicians’ (p.12).

Mindful of such potential variability, and given the findings that a calculation of ES using the pre-treatment Standard Deviation (SDpre) can bias the effect size, particularly if there is a great variability in the range of pre-treatment scores – measures were taken to substantiate the ES analysis. First Hedge’s effect (see Hedges & Olkin., 1985; Field, 2013) was also applied to all Standard Deviation calculations.

Only tiny differences were observed as a consequence (eg. SDpre ES for full validscore population (n=33; MT1=16.5, MT2=7.76, SDpre=5.57; SDpost=4.06) resulted in d SDpre of 1.57, or Hedges g = 1.55, a difference of .02 ). Similarly there was only a .03 difference for SDpre and 0.02 SDpooled also.

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Even if using SDpre to calculate effect size can bias the result if there is wide variability in a pre-treatment population, as per Seidel et al., (2013), this was the methodology employed across the benchmark too (Andrews et al., 2011), suggesting that the result of clinical equivalence and better clinical outcome was correct in relative terms at the very least.

Also, it’s interesting to note, that the pre-treatment group in Andrews et al., (2011) had a larger mean pre-treatment score and bigger standard deviation (Mpre=19.41; SDpre=7.13) suggesting a greater variability of score dispersal than that in the current study (Mpre=16.48; SDpre=5.57). Any bias that may have occurred would, therefore, be more likely to favour the benchmark result’s Effect Size rather than this study’s, thus reinforcing this study’s relative clinical outcome.

There has also been much debate about the measures of subjective wellbeing (SWB) employed in evidence-based research and their efficacy and accuracy in measuring what they say they do (e.g., Eid & Larsen, 2008). Correlations between measures of SWB instruments can be moderate or high. Indeed, Werbart et al., (2013), found response variability between different SWB instruments for the same clients. SWB instruments, however, have been found to provide a practical and feasible way for psychotherapists to track general clinical change especially since diagnostic comorbidity occurs among more than 50% of clients in a typical clinical caseload (Rodriguez et al., 2004). The co-morbidities present for the clients in this study echo this trend.

The employment of CORE-10 in this study was a pragmatic and appropriate measure to use in a prospective study in private psychotherapeutic practice and indeed was dictated in order to facilitate direct comparisons with benchmarked and similar studies (Andrews et al., 2011, 2013; Clark et al., 2009). It could be argued that use of a second measure against which CORE-10 findings could be tested, as a crossreferencing analytical exercise, may have further bolstered the design and its conclusions.

However, a balance as Wampold (2015) argues amongst others (Reese et al., 2014; Miller et al., 2013) needs to be struck between longer and more clinically detailed outcome measurement and feasible measurement within day-to-day clinical practice.

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Given that sessional measurement has been found to facilitate data completeness (Clark et al., 2009; Ionita et al., 2015), brevity and ease of use in the instruments employed within real world settings is a guiding principle (Wampold, 2015; Duncan, 2012; Miller, Duncan, Brown, Sparks, & Claud, 2003).

Perhaps, the balance struck by use of CORE-10, with the inclusion of the short PRN F5 questionnaire on client experience, given the large evidence base underlying the importance of the therapeutic alliance (Bordin, 1979; Horvath and Symonds, 1991), and the salience and centrality of the client perspective (Duncan & Reese, 2015), was appropriate. (I shall return to a discussion of the findings of the PRN F5 later in this section).

Ending as a Moderating Influence In overall terms then, accepting the use of measures and analysis applied, the study provides evidence of a significant, positive, pre-post treatment change for clients of a different ethnic group (Irish, in Ireland), reporting clinically equivalent or indeed quicker reliable improvement and recovery than those in UK context.

It is interesting to note that when the results of the whole population with valid preand post- treatment scores were analysed to see if the type of therapy ending had an impact on the outcome, the result wasnâ&#x20AC;&#x2122;t significant â&#x20AC;&#x201C; even though it had been found to be a key moderator in other similar studies (Andrews et al., 2011; Andrews et al., 2013, Andrews et al., 2015). This may be accounted for in part by the size of the sample and the context of the study set within a small private psychotherapeutic clinic where people were paying for treatment â&#x20AC;&#x201C; perhaps they had a greater incentive to continue to attend and had more supports to facilitate that attendance by comparison to the more socioeconomically deprived context of Clark et al., (2009)? This is a contention that literature around socioeconomic deprivation could be seen to support, (Pan, Stewart, & Chang, 2013; Reiss, 2013). Indeed, the socioeconomic background for those in Andrews et al., (2011) was also deprived, which could underscore a more affluent cohort as a potential driver of the better-than-benchmark results. However,

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the results were also stronger than those in Andrews et al., (2013) which was not as deprived as either of the other two. And so, it’s not a sufficient explanation.

It could be argued that the use of routine alliance measures via the PRN F5, which Andrews et al., (2011) called for in order to facilitate clients remaining in therapy, also advocated in multiple other studies (e.g., Duncan & Reese, 2015) which was applied in this cohort, may have been a factor in delivering the low unplanned ending score and keeping people in therapy. Interestingly, when looking at the percentage of clients who delivered PRN F5 feedback, a greater percentage of the whole sample delivered back post-treatment PRN-F5 feedback (55%) than the reliably improved and/or recovered group (50%). So the trend that Clark et al., (2009) empirically tested, that people with a positive outcome are more inclined to deliver feedback, was not found in the present study. Be that as it may, the fact remains that ending was not a moderator of change for the population as a whole in this study, which could suggest that incomplete treatment didn’t significantly impact on treatment effectiveness.

However, when examined as a moderator of change for the clinical group (n=28), the type of ending did generate a large effect (d = 2.29), which was shown to be just clinically significant (p = 0.0492). This was the moderating effect delivered by taking out only five non-clinical cases from the analysis. This could suggest that clinical levels of distress are particularly sensitive to the unplanned ending moderator - when clients are clinically distressed there is simply more work to be done. This contention is borne out in research on pre-clinical conditions taking less time to treat than those at a clinical level (see for example Korte et al., 2016 on treatment of pre-clinical PTSD; and Aune & Stiles, 2009, on anxiety). Moreover, other researchers have pointed out that in real-world practice, clients typically self-regulate – as they feel better, they end treatment (Baldwin et al., 2009; Leichsenring & Rabung, 2008).

While the small number (four) involved in this study makes a powerful analysis elusive, the small number does facilitate a direct understanding of the reasons behind treatment being discontinued. Of note is that two of the four ‘unplanned ending’ cases were for logistical reasons largely rather than for reasons of dissatisfaction or frustration at a lack of progress with the therapy itself – or so it was reported to their respective therapists. It underlines the need for care in interpretation of this as a

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moderator, which the blunt umbrella of ‘unplanned’ doesn’t necessarily incorporate.

Or perhaps indeed, the unplanned endings were more in line with the findings of other research - when clients feel there is not much further to be gained by continuing, they make the decision to stop (Baldwin, Berkeljon, Atkins, Olsen, & Nielsen, 2009; Stiles, 2013).

Problem Duration In seeking to understand the large pre- and post- treatment effect size found by the study, one must also consider the kind of problems treated – their duration and severity. It is clear that the problem duration treated was relatively extensive with 85% of the whole population experiencing symptoms for more than six months. This problem duration is particularly important given the evidence underlining the possibility of spontaneous or ‘natural recovery’, without any intervention, occurring with under six month problem durations (Catalan, Gath, Edmonds & Ennis, 1984; Kendrick et al., 2006). Without a wait list control, one can’t definitively assert that these cases mightn’t be ones of spontaneous recovery, particularly given the lower pre-treatment mean score relative to benchmark.

Looking just at the clinical population in this study (n=28), in order to ameliorate the effect of non-clinical cases, we find that only one of the cases is of under-six months duration, [albeit with an average pre-treatment score of 19, in line with that of Andrews et al., (2011), for clinical population assessed with CORE-10]. For the clinical group for which we have problem information then (n=27), 96% have problem symptoms for over 6 months. Of this clinical cohort, while 11% of them had enduring symptoms, a third of the clinical cohort had been experiencing symptoms for more than 3-5years, while just over one third (37%) had been experiencing them for over a year. So, while not as enduring as the symptoms being reported in Andrews et al., (2011; 51.1% of depressed cohort and 59.6% of anxious cohort), problem duration was significant.

Looking at literature on natural recovery rates, it shows that recovery rates vary but that in cases of recent onset depression and/or anxiety in primary care, recovery rates

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of 50-70% over the few months following a modest GP ‘treatment as usual’ which didn’t include formal psychotherapy have been reported (Catalan, Gath, Edmonds & Ennis, 1984; Kendrick et al., 2006). In contrast, studies concentrating on cases with a prior duration of 6 months or over tend to report very low recovery rates in wait list samples. Posternak & Miller, (2001) did a meta-analysis of wait list control groups and found that the average recovery rate from depression was approximately 20%. In studies looking at CBT treatment for anxiety for patients with problem duration exclusively of longer than six months, recovery rates rarely exceed 5% on the wait list (Clark et al., 1994, 1998, 2006; Ehlers, Clark, Hackmann, McManus, & Fennell, 2005).

Thus, while a causal relationship without a wait list control cannot be drawn, with a problem duration of greater than six months for 96% of the clinical population of this study, given what literature suggests, it is unlikely that their recovery could be accounted for by natural recovery alone.

Problem Type In line with other research in primary care and real world practice in general, problems were manifest as co-morbidities rather than standalone in most cases (Staines, 2007, Goodheart et al., (2006); Kazdin 2008), with anxiety and depression the most prevalent problems (Clark et al., 2009). This pattern is in line with reported patterns of distress in Ireland (McDaid, 2013). The level of clinical improvement and recovery is comparable to and indeed surpasses, in terms of session number and preand post- treatment effect size, the results of the benchmark and indeed other comparative studies (Mullen et al., 2006; Clark et al., 2009; Andrews et al., 2011; Andrews et al., 2013; Andrews et al., 2015). While Tsarousha et al., (2012) discovered that HG Therapy was effective within one or two sessions, as compared to TAU (treatment as usual) control, this was for the treatment of mild to moderate depression only, whereas the sample here consisted of a wider range of problems being treated.

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In terms of more general comparability, of the 400 plus psychological treatments currently in practice (Corsini & Wedding, 2011), Cognitive Behavioural Therapy (CBT) is the form of therapy with the largest evidence base (Roth & Fonagy, 2005). It also shares many characteristic interventions and practices with Human Givens therapy. Meta-analyses have shown CBT, including computerized CBT, to be effective for individuals with depression and anxiety [Andrews et al., 2010; Butler et al., 2006]. Evidence from effectiveness trials supports the use of CBT in clinical settings also (McHugh & Barlow, 2012) and it has been adopted by NICE for treatment of anxiety and depression, albeit with variations of therapies recommended for differing manifestations of distress (see NICE, 2005). While contrasting methodologies and measurement tools, benchmarks and session number muddy absolute direct comparison, effect sizes from effectiveness trials have been found to be roughly comparable to those obtained in efficacy studies. Indeed, the effect sizes in this study (entire population: n= 33, d =1.56; and clinical population: n= 28; d = 2.29) are significantly higher than those reported by Butler et al., (2006) for example. In 16 meta-analyses of 332 studies, they found an overall effect size of 0.95, a finding that Carr (2007) reports as consistent with â&#x20AC;&#x153;major narrative reviews of the extensive literature of the efficacy and effectiveness of behavioural and cognitive behavioural therapies with a wide range of child, adolescent and adult disorders.â&#x20AC;? (p.16), referencing (Emmelkamp, 2004; Hollon & Beck, 2004).

Moreover, in a meta-

analysis of 21 controlled studies of acceptance and commitment therapy (again with many parallel processes to Human Givens Therapy) involving patients with a range of problems including depression, anxiety disorders, personality disorders, psychosis and other problems, Hayes et al., (2006) obtained a weighted effect size after treatment and at five months follow-up of .66.

Notwithstanding the fact that the effect size is one reflecting five months follow-up â&#x20AC;&#x201C; which is not the case here, it is based on immediately post-treatment data - the effect sizes in this study are considerably in excess of those discussed in these metaanalyses and reviews. And they are well in excess of those found by Clark et al., (2009) based on CBT interventions and Andrews et al., (2011; 2013) based upon Human Givens Therapeutic interventions.

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Caution of course needs to be applied in acknowledging that a definitive comparative reading to other treatments could only be concluded from a controlled comparative study. The benchmarking method does, however, demonstrate comparative treatment to that measured in pre-post clinical assessment with the same, psychometrically sound measures (Nielsen et al., 2004; Minami et al., 2009; Andrews et al., 2011; Andrews et al., 2013; Andrews et al., 2015) – albeit based, as in this case, on a smaller sample size.

It is interesting to note that PTSD, or PTSD with anxiety, or sub-threshold trauma with anxiety, were relatively prevalent in this study population at 24%. The treatment of ‘anxiety’ and ‘depression’ were focused upon in the Luton study (Andrews et al., 2011) and ‘trauma’ made up only 7% (197 cases) in the five-year extension study (2013). Moreover, PTSD and OCD were excluded from the ‘Doncaster’ cohort, (which delivered 87% of the clients with pre-post treatment scores upon which the conclusions in the study are based) in Clark et al., (2009), the benchmark against which Andrews et al., (2011) tested its effectiveness. In general, treatment for PTSD can take longer than anxiety and depression. Craighead & Nemeroff, (2005), for example, argued that in order to treat people with trauma – particularly those with a history of child abuse and neglect – a more intensive treatment may be necessary, which can take longer in duration. Moreover, while there is a body of evidence around the efficacy of CBT treatment for PTSD, the level of evidence around treatment effectiveness and treatment trajectories for pre-clinical PTSD is ‘nascent’, (Korte et al., 2016, p.). However, in their exploratory research, Korte et al. noted that the treatment of preclinical PTSD with a particular CBT intervention (behavioural activation, in vivo exposure and imaginal exposure techniques, the latter as per Foa, Hembree and Rothbaum, (2007) – all interventions common in HG) was marginally quicker.

In the light of this, are the results of this study in terms of rate of change and recovery in the treatment of PTSD, as well as depression and anxiety in the field, particularly strong?

A nuanced understanding is necessary and three key points would temper such a conclusion: Firsly, in relation to the reliability of problem information in this

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prospective study, problem definitions are not as reliable as those delivered within an RCT, for example, with the strict controls and diagnoses such trials demand. The problem categorizations employed here are just that – working conceptualizations, arrived at by the three therapists and have not been subject to strict diagnostic criteria or testing. (This was also true of those in Andrews et al., 2013).

Secondly, research is now pointing to the prevalence of anxiety and depression diagnoses which do not capture PTSD or trauma aspects or diagnoses with which they are frequently co-morbid (e.g., Zimmerman & Mattia, 1999). It’s been postulated that sometimes without sufficient knowledge of PTSD or a strategy for focusing upon it, treatment staff may allow a more established diagnosis or problem definition to come to the forefront (Al-Saffar, Borga, & Hallstrom, 2002). So, there may well have been underlying or non-diagnosed PTSD and trauma in other cohorts with primary diagnoses of ‘depression’ and ‘anxiety’.

Thirdly, and following on from the last point, as Human Givens therapists trained to identify and treat trauma as a fundamental part of their approach, it may be overrepresented as a problem described by therapists, compared to the GP diagnoses in Andrews et al., (2011) or hypnotherapists in Andrews et al., (2015). However, such a bias was not apparent in the 5-year study of Human Givens therapy (Andrews et al., 2013) where it was identified as a problem by HG therapists in only 7% of cases.

Notwithstanding these qualifications, it is notable that the results of the study in terms of rate of reliable change and recovery were achieved when therapy was applied to a significant proportion of clients reported to be suffering from PTSD, pre-clinical PTSD or trauma and anxiety, as well as the more prevalent anxiety and depression reported to be found commonly in primary care contexts. At any rate, a deeper analysis based on more specific reliable details on problems generated by tighter diagnostic or description protocols, would merit further research in the future.

Problem Severity Problem severity is another aspect that deserves attention. While Andrews et al., (2011) utilised therapist reports of severity, that data wasn’t available in this study.

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But, by using CORE-10 scores as a measure of the severity of distress being experienced, we see that the pre-treatment mean is 16.48 for the full population with pre- and post- treatment scores as compared to 19.41 in Andrews et al., (2011). So, could part of the reason why fewer sessions were needed in this study compared to benchmark be a function of the slightly lower clinical score and therefore lower level of distress of the cohort? Literature has established that sub-clinical symptoms are more amenable to quicker change with evidence-based brief interventions (eg. Aune & Stiles, 2009; Gardenswartz & Craske, 2001; and explored by Krone et al., 2016).

In order to account for the discrepancy in pre-treatment score for the whole population, we compared the clinical cohort’s results to the benchmark as a more direct comparison (Dublin: n=28; Mpre= 18.41; SDpre= which is within 1.3 points of CORE-10 pre-treatment mean in Andrews et al., (2011) (n=107: Mpre=19.41, SDpre=7.13) and 0.4 points of Clark et al., (2009) (n=232; Mpre=18.5, SDpre=6). When doing so however, the ES was higher again (d=2.29) and significant (pvalue<0.0001). So, the higher clinical pre-treatment score did not dilute the degree of effectiveness. In fact it went in the opposite direction. While this may capture a regression to the mean propensity, particularly given sample size, it is arguable that it brings us closer to reading the greater rate of change and recovery for the clinical population in this study as a mark of greater effectiveness, as compared to those in Andrews et al., (2011) Andrews et al., (2013) and Clark et al., (2009).

It may be that this could have been in part a function of a cohort drawn from a higher socioeconomic group in which greater supports were available? It’s not possible of course to draw conclusions from that in this study, but it is well established in literature that an inverse relationship exists between socioeconomic status and wellbeing or mental health (e.g., Jokela, Batty, Vahtera, Elovainio, & Kivimäki, 2013; McLaughlin, Costello, Leblanc, Sampson, & Kessler, 2012; Pan, Stewart, & Chang, 2013; Reiss, 2013). And so the argument shouldn’t be discounted without being corrected for.

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Dose-Effect Size The results obtained on the pace of recovery bear comment. Both in comparison to benchmark, Andrews et al., (2013) and Clark et al., (2009) these results are better with reliable improvement and recovery for the clinical population happening in an average of 3 and 3.25 sessions, respectively. Particularly given that the duration of problems suggests that natural recovery is not the most likely explanation, the rapidity of recovery is notable. All the more so given that Howard et al., (1986) in their seminal study found that 14% of clients improved before their first therapy session, 53% improved after eight weekly sessions and 75% were improved by 26 sessions.

Even allowing for the more stringent criteria of improvement demanded in more recent research (e.g., Jacobsen & Truax, 1991) the conclusion reached in an efficacy study involving more than 2000 clients (Hansen et al., 2002) was that more than 8 sessions are required for 50% of cases to show improvement. Shadish et al., (2000) in their meta-analysis of 90 studies of adult psychotherapy under clinically representative conditions found a correlation between therapy dose and effect size.

And so this studyâ&#x20AC;&#x2122;s findings buck that trend.

However, both Andrews et al., (2011) and Andrews et al., (2013) on HG interventions in primary care had established reliable clinical improvement and recovery in a shorter number of sessions (4-5 sessions) than the eight, the suggested requirement for 50% of cases to show such improvement (Hansen et al., 2002). The sample size of the clinical cohort (28) in this study, it must be noted, is small and replications would be required with a larger sample.

Client Satisfaction: Common factors considered Research demonstrates that the clientâ&#x20AC;&#x2122;s perception is the most salient to the outcome (Klein et al., 2003) in that it is their subjective reading of whether they feel better or not that is most crucial, and that there can be divergence in perception between client and therapist (Gaddis, 2004; Gordon, 2000). In effectiveness research, in which SWBs are employed to measure the outcome, the clientâ&#x20AC;&#x2122;s perspective is the

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perspective. Full stop. (Indeed I would argue that in an endeavour designed to address and ameliorate distress, the client’s perspective is the perspective, no matter what measure or methodology is being employed to assess outcome). Including a measure to capture clients’ perception of the treatment here was therefore essential, and also served as a device to seek to keep clients in therapy and facilitate their recovery and data completeness [as recommended by Andrews et al., (2011) following Clark et al., (2009)].

We used PRN-F5 as a measure of client satisfaction with their therapeutic experience.

The results of the PRN-F5 measure are undoubtedly positive in this study, illustrating a high level of satisfaction for the whole sample for whom scores are available with the treatment at both entry (M=37.26; 93%), n=27) and exit (M=37.8; 94.5%, n=27).

And indeed available scores for the full recovery sample were higher again (M=38.87; 97%, n=15) at entry and exit (M=38; 94.5%, n=15). While this is illustrative rather than evidential, it reflects a high level of client satisfaction with his/her experience of therapy: feeling heard, feeling the content under focus was relevant, feeling helped by sessions, and happy with the service received. These reflect key elements of the Therapeutic Alliance.

The alliance, as conceptualised most clearly by Bordin (1979; 1976) incorporates the therapist and client’s agreement on the goals of the therapeutic engagement and the tasks necessary for them to be realised and the bond underpinnning both. Some have argued that it is the force for therapeutic change (Rogers, 1980), others that it facilitates the successful application of therapy (Beck et al., 1979). Much research – over 7,000 papers by 2010 (Horvath et al., 2011) - has been dedicated to examining the therapeutic alliance and seeking to tease out the constituents within it that drive change. Extensive research has found a significant relationship between therapeutic alliance and the therapy process and outcome (Martin et al., 2000; Norcross, 2002; Zuroff & Blatt, 2006). The alliance has been found to be one of the most robust predictors of positive psychotherapy outcome, regardless of type of therapy utilized or whether assessed by therapist, client or independent observer (Horvath, 2001).

If understood as a prerequisite to therapeutic change, as per Ackerman & Hilsenroth, (2001) and Dobson & Dobson, (2009) for example, the PRN-F5 results could be read as potentially illustrating client-perceived positive therapeutic alliance and thereby

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underscore the potential for the therapeutic intervention itself to have engendered the outcome. If understood as an exclusive driver of change, (as per Wampold, 2001, for example) the PRN-F5 results could be seen to dilute certainty that the interventions themselves were responsible for the pre-post treatment changes observed and rather that the results arise from an application of the common factors of all good therapeutic interventions.

The relationship between PRN F5 feedback and session number was also explored but no statistically significant relationship was found, suggesting that in this study at least it wasnâ&#x20AC;&#x2122;t a significant moderator of change. However, the sample size to enable conclusions to be drawn must be highlighted as a limitation to reaching a conclusion.

Which brings us neatly to the role of therapist as a potential moderator of change.

Therapist as a Moderator of Change No discussion of the results of any effectiveness study could discount the potential effect of the therapist as a key moderator of change. A large body of evidence has amassed to underline the significant variability in outcome among individual therapists, suggesting that the clinician accounts for a greater percentage of effectiveness than does the client-rated alliance (5%-15%; Crits-Christoph, Gibbons, Hamilton, Ring-Kurtz, & Gallop, 2011; Horvath, Del Re, Fluckiger, & Symonds, 2011); treatment approach (0-1%; eg Wampold, 2001; Wampold & Brown, 2005), or use of an empirically supported treatment (0%-4%; Wampold, 2005). Indeed, Kraus et al., (2016) argue that â&#x20AC;&#x2DC;the current state of research strongly suggests that therapist differences are highly relevant to any effort aimed at providing clients with optimal treatment (p.474).

Could the better-than-benchmark results in this study be a product of high therapist performance?

While this study includes data from three contributing therapists, one therapist supplied just 2 cases with valid pre- and post- treatment scores, one delivered 11 cases, and the third 20 cases. In order to reach any meaningful conclusions on this question, we would need more therapists feeding in data, and over a much greater

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sample size, to be able to examine this factor at a satisfactory level. However, upon comparing the outcome of clinical cases delivered by the two therapists with relatively larger sample sizes, Therapist 1 (19 cases-delivered) and Therapist 2 (7 cases-delivered) the difference between them was not significant. Caution of course needs to be exercised here given the discrepancy in relative sample size and small overall sample size.

This notwithstanding, however, it is interesting to note that Therapist 1 who supplied the majority of cases when comparing the two therapists’ clinical populations (n=19; 73%) is the most experienced amongst the trio. She is the longest qualified, a trained supervisor, and is constantly focused on skill and personal development. The second therapist, delivering 7 clients (27%) has been practicing for four years, and while focused on skill acquisition and personal development, is only amassing the experience to hone her evolving understanding, skill and technique implementation.

In terms of understanding the potential impact of this, it bears pointing out that although it is clinically assumed that some therapists are better than others at facilitating change (Albert, 1997; Jennings & Skovholt, 1999), little has been known about the actual outcomes of individual therapists until very recently. Okiishi et al., (2003), for example, looked at outcome by therapist and concluded that their HLM (Hierarchical Linear Modelling) analysis demonstrated that ‘there are indeed significant differences among therapists in the outcomes of their psychotherapy clients’ (p.370). Interestingly, they found no differences based on gender, type of training, level of training or theoretical orientation. Indeed, other studies have revealed that student therapists were as effective, if not more effective, than more experienced therapists (e.g., Minami et al., 2009).

Moreover, a study published last year (2015), which explores the key mediating factors involved in the development of top-level performers in psychotherapy, postulated that ‘deliberate practice’, analogous to that identified in sports (Cote, Ericsson, & Law, 2005), business (Sonnetag & Kleine, 2000), medicine and surgery (Norman, Eva, Brooks, & Hamstra, 2006), and music (Ericsson et al., 1993; Krampe & Ericsson, 1996) is a model by which to understand high performance in psychotherapy too. Defined by Ericsson and Lehmann (1996) as “individualized training activities especially designed…to improve specific aspects of an individual’s

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performance through repetition and successive refinement” (pp. 278-279) the concept of deliberate practice distinguishes between the experience of routine and proficient execution of skills and targeted skill development just beyond an individual’s current abilities (see Chow et al., 2015).

One could thereby argue, that this is the point at which a developing psychotherapist could be at, given the conscientious application of supervision – and the commitment to filming and reviewing therapy sessions throughout the first year of her practice (correlated with Deliberate Performance in Chow et al., 2015), and the product of an experienced therapist who is continually focused on skill refinement through further study, academic achievement and teaching. For Chow et al., (2015) discovered that consistent with the literature on expertise and expert performance, ‘the amount of time spent targeted at improving therapeutic skills was a significant predictor of client outcomes’ (p.337) rather than just volume of experience.

Furthermore, Kraus et al., (2016) argue ‘the therapist effect is estimated to be even larger in naturalistic studies (7%), for which there is probably greater variability in therapist abilities and styles of treatment delivery’ as compared to the intensive uniform training and adherence to very tight treatment protocols in RCTs (Kraus et al., 2016, p.474). So, whether the product of deliberate practice, or treatment style, or therapist ability, or some such variability, it is distinctly probable that therapist effectiveness is a moderator of change in the current research.

In the context of this study, the number of therapists and sample size keeps this exploration at a discursive level. More data from a greater range of therapists would be required to explore it empirically. Such further exploration would, in my view, be a very interesting avenue to pursue. It is one that could be facilitated by the growth of practice-research-network hubs of data, which could retrospectively be mined to explore such hypotheses (as was the case in Chow et al., 2015).

Outcome Measurement In the burgeoning canon of effectiveness research and outcome measurement, the latest studies are demonstrating a correlation between outcome and the use of the

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measures themselves. The suggestion is that the utilization of outcome measures becomes a moderator of change in and of themselves. (eg. Reese, Norsworthy & Rowlands, 2009; Duncan & Reese, 2015; Chow et al., 2015).

Earlier research on outcome measurement suggested that outcome assessment conducted on a continual basis could lead to increased effectiveness – particularly for those clients who weren’t progressing in therapy as expected (eg Beutler, 2001; Lambert, Hansen & Finch, 2001). This, it was postulated, was a product of routine monitoring of patient responses to both relationship and ongoing treatment enabling relationship alliance repair if necessary (Ackerman et al., 2001), providing opportunities to make necessary changes in treatment or approach (Lambert et al., 2001) and improving relationships and making referrals as appropriate (Brown and Jones, 2005). In the last seven years or so, the research has evolved into suggestions that all clients, not just those projected to do poorly, benefit from using a continual assessment system (e.g., Reese, Norsworthy & Rowlands, 2009; Duncan & Reese, 2015).

So, could it be that the utilization of outcome measures in this study has enhanced the outcome and could account in part for the better-than-benchmark results?

To understand whether that could be the case, one has to understand what elements of outcome measures are intrinsically efficacious. In some studies, it appears that an alarm or warning signal signifying that the patient isn’t progressing as expected is critical (Probst et al., 2013), whereas in other studies that is not pivotal (Amble, Gude, Ulvenes, Stubdal, & Wampold, 2015). The Pragmatic Tracker, the system used in the current study, does incorporate warning signals around gaps in seeing clients, and highlighting scores at risk levels. So, perhaps it did, perhaps it didn’t impact?

Moreover, as Wampold, (2015), points out, ‘it is also unclear whether integrating the information about patient progress in therapy (e.g., by discussing it with the patient) is important’ (p.459) pointing to a reading of Amble et al., (2015). This highlights an issue in terms of ‘feedback’ being cited in the literature. At times, the definition of it is not clear – is that feedback of measures by an outcome measurement system to the therapist, or feedback by the therapist of outcome measures to the patient, or feedback by an outcome measurement system directly to the patient, or all three? In the current

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study, patients filled in their questionnaires electronically, for the most part. They were thanked for their participation but werenâ&#x20AC;&#x2122;t given any score total, or feedback on the meaning of those results or how they were faring automatically by the system.

Any who filled it out on paper were likewise not fed back any summary of progress or score total. So, patient feedback on sessional scores was a product of respective therapist judgment and style, which was not directed for in the study procedure. In terms of therapist interaction with the Measurement System, there was variance there too. One therapist was inclined to feed data into the electronic system in bulk, rather than referring to it session-by-session. Another was more likely to interact with it monitoring session-by-session progress.

Interestingly, studies using the PCOMS system, in which discussing patient progress with patients is endemic to it (e.g., Duncan & Reese, 2015), show particularly encouraging results. Indeed, it is the discussion of progress with the graph illustrations the system produces, that have been highlighted as the most important aspects of Routine Outcome Measurement (ROM), (Amble et al., 2015). Given that this was not directed for, and there was variance amongst style of use, one cannot be definitive in claiming that the use of measures influenced the result in this study.

However, the study would bear replication, with a tighter procedure around outcome measurement use, and particularly, in my view, with a control no-feedback group to explore this in more detail. This could be designed to facilitate differing therapist styles (therapists resistant to interpreting ROM session-by-session in control group and therapists enthusiastic to instigate in comparison group) and thereby accommodate what literature has highlighted as one of the resistances to implementing ROM in general (see Ionita et al., 2016).

Interventions And last but not least, the interventions themselves obviously bear a great deal of further scrutiny. In such a naturalistic study, it is not possible to isolate specific intervention effects. Indeed, therapists were not directed to account for the interventions employed. In future studies, at the very least, protocols could be agreed to catalogue interventions employed in order to allow an understanding of what

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techniques were used, when in the course of treatment, and with what results.

Qualitative assessment as well as quantitative investigation would be useful to understand these effects.

In general, the interventions employed within HG therapy have been empirically tested, so this would not be that gargantuan a task. It would need to be tested as a whole though, in the many combinations alive in practice to explore the effects.

Particular attention, in my view, should be paid to the rewind technique which was used in every single case in this sample and which is felt, anecdotally, to be a massive lever in expediting change and recovery. Similar methodology to that employed using what sounds like a very similar technique - ‘imagery rescripting’ [see Stopa, (2011) for overview; Arntz, (2011), for imagery scripting and the treatment of personality disorders; Hackmann, (2011) for imagery rescripting and PTSD; and Wheatley & Hackmann, (2011), on Imagery Rescripting and depression] could be used to examine its impact.

As ‘selectively eclectic’ as CBT is (as described by Froggatt, 2006), so too is HG, but look how much evidence CBT has managed to amass. The gauntlet has been laid down.

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Limitations

While the studyâ&#x20AC;&#x2122;s findings are very positive, caution needs to be exercised in their interpretation.

First of all, it is not a random controlled trial and therefore, its internal validity is compromised: One cannot say with a reasonable degree of certainty that the results obtained are attributable to Human Givens therapy intervention alone and could not have been generated by an alternative approach for the same or a contemporaneous data set â&#x20AC;&#x201C; given that there was no control arm. Moreover, the lack of waitlist control, while decided upon for valid reasons, similarly undermines the possibility of drawing a causal relationship between this exact therapy and the positive outcomes, given the evidence illustrating spontaneous recovery (e.g., Kendrick et al., 2006). However, this concern is ameliorated somewhat given that 96% of clients had experienced problems for longer than six months (eg., Clark et al., 1994, 1998, 2006; Ehlers, Clark, et al., 2005). And indeed the contextualised hypothesis (as per Leichensrung, 2004), protects against an over-reading of the studyâ&#x20AC;&#x2122;s findings.

The relatively small size of the clinical sample (n=28) compromises the representativeness of its findings, which would have been greater with a larger volume of data to draw from. Interpretation of effect sizes took small sample size into account however.

The three therapists from the one psychotherapeutic private practice self-selected to participate and so it cannot be assumed that they are truly representative of typical HG practitioners or indeed that the clients seen by this particular cohort are typical of those generally seen by HG therapists.

Moreover, given that the study was conducted in a private psychotherapeutic practice rather than in public primary care may suggest bias in favour of higher socioeconomic background clients and therefore not be generalizable to a broader profile base. The comparison to Andrews et al., (2013) with its larger cohorts of clients, who were treated with the same approach, is helpful in easing concern somewhat. Its

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homogenous ethnic profile make-up is similar - white British for Luton and white Irish for Dublin, which facilitates direct comparability.

Follow-up with clients at 3, 6 and 12-month intervals would have been advantageous in determining whether gains made were maintained, particularly given the rapid rate of change and recovery, to ensure that the rapidity of recovery was not just an example of â&#x20AC;&#x2DC;flight into recoveryâ&#x20AC;&#x2122; rather than enduring recovery.

Finally, while all three therapists are trained in the HG approach, there was no attempt to test for adherence to the HG model to ensure beyond doubt that it was the model being implemented and that it was being implemented correctly.

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Conclusion In conclusion, this naturalistic study to assess the effectiveness of Human Givens Therapy for psychological distress in routine psychotherapeutic private practice in Ireland demonstrated results exceeding those against which it was benchmarked (Andrews et al., 2011). Pre- and post- treatment measures showed significant difference with effect sizes of d=1.56 and d=2.29 for the full sample and clinical cohort respectively, and 82% of the clinical population reliably improving while 71% of the clinical group recovered and reliably improved, within 3 sessions on average, exceeded benchmark and Andrews et al., (2013) on all points.

Conclusions are of course tempered by the relatively small sample size but the study is nonetheless a very positive reinforcement of previous studies on Human Givens, which again demonstrates its plausibility as a treatment that works quickly to ameliorate psychological distress, this time in Ireland, for clients attending a private practice.

Given the societal need for interventions to work quickly to help those experiencing psychological problems, and at such a level in this country, as elsewhere, this study is a timely demonstration of an approach worth serious consideration.

Moreover, the benchmarking methodology employed via the collection of outcome measures in routine clinical care offers a potential model for mental health practitioners in public and private practice to secure their own immediate evidencebase. There is also the possibility of feeding into a bigger database to enable meaningful comparisons and thereby, secure higher standards.

That such a methodology can also foster practice-research-networks, which can in turn supply data for more research questions to be explored, which can generate ecologically-valid insights and inform responsible evidence-based practice, is an outcome that both professionals and the public can but welcome.

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Recommendations •

In light of the positive outcomes of this and previous studies (Andrews et al., 2011; 2013; Tsaroucha et al., 2012a), in terms of the small number of sessions necessary to achieve reliable improvement and/or recovery – alongside the high client satisfaction scores in this study – further investigation of Human Givens is recommended as an excellent fit for State-supported counselling services as it is proving to be both cost effective and effective.

•

Replication of this study with a larger sample size taken over a longer period of time, with 3, 6 and 12-month follow-ups. Ideally this would be an Irelandwide HGIPRN, the results of which could be useful in demonstrating the suitability of HG Therapy from an effectiveness, cost-effectiveness and timeefficient basis, to relevant national programmes in primary care. (CIPC, for example). This could in turn be benchmarked against UK-based and international datasets. Study design could incorporate any/all of the following: o Outcome measurement control no-feedback arm and tighter protocol on feedback use o Tighter protocols on problem description o Tighter protocols on intervention description and use

•

The adoption of routine outcome measurement as a basic tool in primary care, whether in public or private practice, to monitor therapist effectiveness and pre-empt and prevent client drop-out and deterioration, thus optimising treatment outcome, accountability, and the maintenance and encouragement of high standards, in line with the aims of WHO 2016, APA, NICE and CPA.

•

For professional representative psychotherapy bodies in Ireland to agree a set of accepted standard national validated and reliable outcome measures for all psychotherapists to use in routine outcome measurement. For those to be fed into a practice-research-network at the level of the regulatory body, or nationally, to develop a national treatment outcome database. This would facilitate, foster and serve people in need on this island; policy makers in

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determining service need, treatment shape, and future service design; researchers in ready data provision facilitating the potential to answer diverse research questions of direct clinical relevance; and a database against which international benchmarking could serve national and international interests.

•

To incorporate routine outcome measurement in training and supervision strategies across psychotherapy approaches to encourage directed and deliberate practice to support psychotherapist development and effectiveness, and thereby, client experience and treatment outcome.

•

An RCT to be conducted of HG Therapy to establish its efficacy.

•

A naturalistic study of the application of the rewind technique with a traumatised population, or, an RCT, with two trauma groups (whether preclinical or clinical PTSD); one treated with Rewind, one with an alternative Pre-clinical/ clinical PTSD treatment to explore its effectiveness/efficacy.

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APPENDICES

Appendix 1.1 HUMAN GIVENS INNATE NEEDS & RESOURCES Emotional Needs Psychologists through the ages also determined that there are other nutritional needs, emotional rather than physical, which are equally crucial for our wellbeing – and sometimes, even for our survival. These include the needs for: Security — a safe territory and environment, which enables us to live our life in safety without excessive or undue fear.

Attention (to give and receive it) — a form of nutrition that fuels the development of each individual, family and culture.

Control and autonomy — having volition to make choices and have control over what happens to us. Exercising volition gives us a form of feedback from the universe that we exist.

Intimacy and friendship — friendship, loving intimacy. Knowing at least one person accepts us totally for who we are, “warts and all” Community — feeling part of a wider community; being part of social groups outside our immediate family. We are a group animal.

Competence and achievement — feeling you are achieving and are competent in at least one area of your life, which builds self-esteem.

Privacy — time and space enough to reflect and consolidate experience Status — a sense that we are accepted by, and valued in, the various social groupings to which we belong.

Meaning and Purpose — this comes from being stretched in what we do and how we think – by service to others, learning new skills or being connected to ideas or philosophies bigger than ourselves. Meaning makes suffering tolerable.

INNATE GUIDANCE SYSTEM – RESOURCES Our innate resources, which can be usefully thought of as our Internal Guidance System, include: Complex Long Term Memory – the ability to develop complex long-term memory, which enables us to add to our innate knowledge and learn.

Rapport and Empathy - the ability to build rapport, empathise and connect with others

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Imagination – which enables us to focus our attention away from emotions and problem solve more creatively and objectively Conscious Rational Mind – which can check out emotions, question, analyze and plan (left hemisphere) The Ability to ‘Know’ - understand the world unconsciously through metaphorical pattern matching (right hemisphere) An Observing Self – that part of us which can step back, be more objective and recognize ityself as a unique centre of awareness separate from intellect, emotion and conditioning (frontal lobes) A Dreaming Brain – that preserves the integrity of our genetic inheritance every night by metaphoricallty defusing emotionally arousing expectations not acted out the previous day.

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Appendix 1.2: Study Consent Form

THE DUBLIN HUMAN GIVENS CENTRE/ Human Givens Institute Practice Research Network

Client Consent Form

I have read the information sheet provided and I agree to the fair and lawful processing of personal information for the purposes of analysis in line with the Data Protection (Amendment) Act 2003.

I understand that the researchers at The Dublin Human Givens Centre and/or The Human Givens Foundation Practice Research Network will not have access to any personal data provided (e.g. name, address, date of birth) which makes the information identifiable to me, and that I will not be identified in any way in anything that is written or reported about the outcome measures or study.

Signature

……………………………………..

Name (block capitals)

…...................................................

Date

………………………………………

I would like to be notified of the results of the study being conducted at the Dublin Human Givens Centre. (tick if you would)

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Appendix 1.3

THE CORE MEASUREMENT SYSTEM CORE is an abbreviation for Clinical Outcomes for Routine Evaluation (Mellor-Clark. J, Barkham. M, Connell. J, Evans. C, 1999) It is the first standardized public domain approach to audit, evaluation and outcome measurement for managers and practitioners working in UK psychological therapy and counseling services. It provides a framework for responding to the increasing demand in health and other sectors to provide evidence of service quality and effectiveness.

CORE was designed as a quality evaluation system to profile and enhance psychological therapy service delivery and development. Consequently, data collation and analysis has a focus that is broader than 'outcome' and incorporates wider factors that include client context and therapy processes. These context and process factors have been allied to models of quality assurance that place health outcomes and/or clinical effectiveness in the context of factors such as service accessibility, clients' attendance efficiency, and therapy appropriateness.

Users of the CORE System Increasingly, all providers of psychological therapy across both public and private sectors are being asked to provide evidence of their service effectiveness and overall quality.

In the National Health Service CORE is used to help risk assessment and meet clinical governance and performance management requirements.

Psychiatrists, psychotherapists, clinical psychologists, counsellors and other professionals providing talking therapies across NHS primary, secondary and specialist care use CORE System tools and CORE-PC software.

Outside the NHS, workplace counselling, student counselling, palliative care, drug and alcohol services, and private practice are all sectors from which the CORE System has attracted enthusiastic supporters to the CORE Benchmarking Network. The main advantage of using a standardized system for monitoring and managing service delivery is that it offers the opportunity to compare your service profile with national benchmarks that provide comparative indicators and descriptors.

CORE-OUTCOME-MEASURE based on 0-4 Likert Scale, assessing, â&#x20AC;&#x2DC;over the past weekâ&#x20AC;&#x2122;:

Symptoms

Anxiety

Depression

Physical problems

Functioning

General

Close relationships

Social relationships

Risk

To self

To others

trauma

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CORE-10

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PRN â&#x20AC;&#x201C; F5

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Appendix 1.5: Therapist Briefing

Psychotherapist Process A Naturalistic Study to assess the effectiveness of Human Givens Psychotherapy in private practice in Ireland. February â&#x20AC;&#x201C; May 2015/ Feb â&#x20AC;&#x201C; June, 2016 .

On meeting client for the first time: -

-

-

Invite to participate in this study, being undertaken as part of a Masters programme in Psychotherapy by one of the practicing therapists at the Centre Emphasise that there is no compulsion to do so. Treatment proceeds regardless.

If client is interested, go through the client information sheet, emphasising: o Confidentiality o Treatment proceeds regardless o Involves two questionnaires and one feedback sheet to be filled in each time o These can be completed electronically or in hard copy If interested, client asked to sign the consent form Therapist goes through the first baseline measures (CORE-10) with the client within the session (using it for immediate information gathering) Feedback sheet (PNR-F5) is given to the client upon leaving. They are asked to fill it in and drop into the box in the waiting room/ bring it back with them at the second visit/fill in electronically Clients are told that the CORE-10 questionnaire will be sent to them a couple of days in advance of their next appointment. They will be asked to fill them in electronically. If they arrive without having done so, they can be asked to fill in before their next session proceeds.

Send the questionnaires to each client two days in advance of following session After last session, all are filled in again. Ask client to email questionnaires back or fill in there and then and post into box in waiting room.

On using Pragmatic Tracker Introduction - If at all possible, immediately input information electronically.

- If client has not emailed questionnaires in between, please ask them to fill in electronically in the waiting room (ipad will be left there). If uncomfortable

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with this, hard copies will be there for them to fill in alternatively. Hard copies can be posted into box.

Each psychotherapist sets up his/her own clients on the Pragmatic Tracker system (in advance, during or afterwards, depending on circumstances).

Content - Input new client into your practice category (you will only be able to access your own clients) - Code ID formula: client initials, date, apt time (eg Joe Brown, 4 feb at 10am = jb04021510 (This means will be able to cross reference with diary entries).

- Within Session List o Identify problems presenting (this generates new measures â&#x20AC;&#x201C; likert scale 1-10) o In session notes, list treatments given only, separated by comma (eg: psychoeducation, guided imagery, story)

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Appendix 1.6 CLIENT Study Briefing This Practice Research Network uses a standard evaluation system (CORE) and two more measures (HG ENA and the PRN 5). These help providers of counselling and other psychological therapies to deliver and develop the best possible services to clients seeking help for their difficulties and concerns.

As part of the system, all clients are asked to fill out a short questionnaire before and after their therapy sessions. These questionnaires help us in understanding your problems, and ultimately, how we can best help you with those problems. You can fill these questionnaires out either on paper or directly into an electronic database. This database is secure and dataprotected.

We hope you will agree to fill out the questionnaires, but would like to emphasise that taking part is entirely voluntary and declining to complete them will not affect your access to therapy in any way.

About our evaluation: •

•

•

•

•

• •

We would like you to fill out a brief questionnaire before and after your contact with the therapy service. Your therapist may also fill out simple forms relating to your therapy To help in monitoring your ongoing progress and how well the therapy suits you, your therapist will also ask you to complete some very brief forms (CORE 10) at each session, at the beginning and at the end.

The purpose of these forms is: o 1) to help us better understand more about your problems that you wish to address in counselling, o 2) to help us directly in our work with you. o 3) to help us learn how best to improve our services. o 4) to disseminate lessons learnt to other interested professional associates through publication.

The processing of completed forms is co-ordinated by a therapist in the Dublin Human Givens Centre, who is currently studying for a Masters degree in CBT. The data will also be input to the Human Givens Institute Practice Research Network to assist us further in the development of our services.

The information from the forms will be treated as strictly confidential. No names or personal details of any kind are used on any forms that leave your personal records.

Filling in the forms will be taken to represent your agreement for the anonymized data to be used for service evaluation.

Research clearly shows that ongoing feedback from the client about the therapeutic relationship and how change is progressing in therapy assists in improving the outcome of therapy. However, we wish to emphasise that taking part in this feedback is entirely voluntary and should you, at any point, choose not to fill out the forms it will not affect your access to therapy.

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