Mitigating Poor Therapy Outcomes

1 Article Critique The Rationale for Article: The article by Harris, Mitchell, Tabet, and Hundley (2020) describes a study investigating the impact of individual factors such as gender, education, distress, social role, and age, among others, on client attrition. The researchers noted that although community counseling plays an essential role in improving the social and psychological health of people, over 20% of clients drop out of counseling prematurely. They also noted that attrition rates are higher among populations suffering from certain conditions such as personality disorders. Therapists, therefore, need to understand the factors that might lead to dropping out from a counseling program and implement appropriate measures to mitigate poor therapy outcomes.

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2 Clients who drop out may also experience a relapse of distressful symptoms and inappropriate behaviors hence the need to avert such eventualities (Harris, Mitchell, Tabet & Hundley, 2020). Few studies have investigated the causes of premature dropout of clients from community counseling programs. These studies focus mostly on personal characteristics. Recent studies are trying to expound on this issue by looking at client-counselor relational factors. In the current study, the researchers wanted to dig deeper into the causes of attrition. The researchers used a community sample of adults who attended a University-Based Community Counseling Clinic. The factors identified as potential contributors to clients' attrition include socioeconomic status, ethnicity, social and psychological impairment at the beginning of counseling and education level.

Research Questions/Hypotheses The researchers have explicitly stated the purpose of the current research. The researchers intended to generate comprehensive information on predictive, need-based and enabling factors that lead to client attrition to add to existing literature (Harris, Mitchell, Tabet & Hundley, 2020).

The researchers did not provide a clear hypothesis for their work. They, however, provided a clear research question that was used to guide the study. The research question was intended to generate information on the predictive capacity of gender, education, ethnicity, and support on client attrition (Harris, Mitchell, Tabet & Hundley, 2020). The researchers intended to use the findings from the current study to provide information on factors that increase the risk of client attrition and implications for counselors assessing clients for these factors. The study found out that attrition was contributed by interpersonal relationships, social support, and the educational level. These findings indicate that the researchers were able to answers the research question.

Variables, Concepts, and Constructs:

3 The researchers have identified the variables used in this study. These variables include the level of education, the race/ethnicity of the client, the social support of the client particularly the number of people in the client’s social support circle, the client’s gender, age, and disruptive symptoms at the onset of counseling (Harris, Mitchell, Tabet & Hundley, 2020). These factors are the independent variables and were referred to as the predictive variables in the study. The dependent variable in this study was client attrition. The researchers wanted to determine whether the predictive factors could lead to client attrition.

The study was based on a quantitative method whereby binary logistic regression and cross-sectional data were used. The researchers investigated the predictive implications of eight variables on adult client attrition. The phenomenon that could be the subject of qualitative research in the future is adult clients' satisfaction with community-based counseling. This phenomenon would require a qualitative method since it would involve investigating individuals' perceptions of counseling.

4 Methodology: Data Collection The institutional review board approved the study after demonstrating compliance with the research ethics. The investigation was based on an analysis of archival data. The researchers utilized convenience sampling to retrieve archived clinical records of couples or individuals who attended a community counseling clinic. The investigators focused on the clinical records between January 2011 and December 2016. The study sample comprised 1781 adult clients who attended counseling services. The sample included individuals of all ages, gender, and socioeconomic backgrounds (Harris, Mitchell, Tabet & Hundley, 2020).

Concept Assessment The researchers collected data on the eight predictive variables were collected from all the participants. The researchers noted the age, gender, type of counseling, race, level of social support, education, symptoms of distress, and the participants' interpersonal relations. However, some participants did not provide data on the predictive variables. The researchers indicate the specific numbers of the participants who provided data on each of the predictive variables.

Analysis The researchers used the binary logistic regression (BLR) in SPSS (Version 24.0) to analyze each variable's data to answer the research question. The BLR was preferred in the current study since it can determine how the independent variables collectively influence the dependent outcomes. This analytical method can also determine if the termination was a personal choice or influenced by external factors. Another beneficial aspect of the BLR is that it can provide reliable information concerning how predictive factors influence dependent outcomes

5 (Harris, Mitchell, Tabet & Hundley, 2020). The statistical model was suitable for determining the influence of the predictive variables on client attrition.

External Validity and Generalizability External validity describes the extent to which the researcher can generalize the study findings to other situations, people, and settings. Findings with low external validity cannot be generalized to other settings. The factors that influence external validity include time, research environment, and population characteristics including size, subject selection, and interactions.

The threats to external validity include situation effect, election bias, experiment effect, and Hawthorne effect. The researchers used a large study sample of 1781 subjects to increase the external validity of the results (Harris, Mitchell, Tabet & Hundley, 2020). The researchers used convenience sampling whereby they gathered clinical data of individuals from different backgrounds for analysis.

Internal Validity Internal Validity describes the extent to which a trust-worthy cause and effect relationship is established between treatment and outcome in a study. The presence of internal validity eliminates the possibility of alternative explanations for the research findings. The researchers used a large study sample to reduce the threat to internal validity. The researchers did not group the participants according to extreme scores. The researchers also utilized a highquality statistical model for data analysis.

Identification, Description, and Critique of Conclusions: The researchers concluded that the identified independent variables predicted client attrition. The researchers pointed out that education, number of supports, and IR scores were the primary contributors of attrition. These findings are supported by the fact that persons with lower

6 support numbers, higher education levels, and a greater level of interpersonal distress are likely to drop out prematurely (Harris, Mitchell, Tabet & Hundley, 2020). Notably, however, the study found out that people with higher educational levels were less likely to drop out. This finding suggests that attrition occurs due to a combination of factors.

Applications The researchers have identified the critical factors that contribute to the premature termination of counseling programs. Therapists can utilize the findings of the current study to identify individual factors that might lead to client attrition. This information can assist in the development of a counseling program that supports all populations with minimal attrition.

Counselors can incorporate information obtained from this study into practice by considering the client’s level of education, age, gender, social support, and ethnicity.

7 References: Harris, S., Mitchell, M. D., Tabet, S. M., & Hundley, G. (2020). Predictors of Client Attrition in a University‐Based Community Counseling Clinic. Journal of Counseling & Development, 98(1), 74-82. https://doi.org/10.1002/jcad.12301