McGill Psychology Undergraduate Research Journal Issue XIII April 2022
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Table of Contents
09 21 Editing Team, 2021-2022 Megan Kuo Nicole Basran Alexa Nordine Célia Sciandra Divi Maheshwari Ezelbahar Metin Gabriel Rodrigues
Physical Activity and Emotional Well-being in Canadian Adolescents
The Relationship between Mindfulness Facets and Aggression in Young Women
The Effect of Perfectionism on the Experience of Anxiety During the COVID-19 Pandemic
Ignacio Perez Montemayor Cruz
Perfectionism, Self-Care, Motivation, and Depressive Symptoms in University Students During the Pandemic
Dear Reader, Every year, the McGill Psychology Students Association (MPSA) publishes an edition of the PSI Journal to give students an opportunity to showcase their research, and give readers like you a glimpse into the wide variety of student work being conducted here at McGill. While the last few years have had a lot of ups-and-downs, our wonderful team of editor’s and I are happy to bring you the 2021 - 2022 edition of the PSI Journal. This volume contains work that our team of seven editors had the pleasure to read, edit, and now present to you. To all of the students who submitted, regardless of acceptance to the journal, we thank you for the time and effort you put into your work. Although it was a painstaking process deciding on which applications to accept, it was an honour to read through all of your work.
I would like to extend my thanks to the executive members of the MPSA for answering all my questions, and coming to my aid whenever it was asked of them. Thanks as well must go to Prof. Mathieu Roy for signing on once again to be our Supervising Professor for the year. We couldn’t have done it without any of you. On behalf of our team, I hope you enjoy our selections as much as we did. Happy reading, Matthew Campbell Editor-in-Chief
Physical Activity and Emotional Well-being in Canadian Adolescents: Evidence from the Health Behaviour in School-Aged Children Study
Karine Talbot Department of Psychology, McGill University PSYC 395: Psychology Research Project 1 Dr. Frank Elgar April 16, 2021
Abstract The Canadian guidelines for physical activity state that children and adolescents should participate in at least 60 minutes of moderate to vigorous physical activity every day of the week. Despite this recommendation, results from the Health Behaviour in School-aged Children (HBSC) study reveal very few children meet these guidelines. Considering the physical and mental health benefits associated with regular physical activity, school-based interventions are necessary to address lack of participation and support youth’s emotional well-being. The current study derived data from the Canadian HBSC 2017-2018 cycle (N = 21, 750), which observes the health and well-being, social environments, and health behaviours of youth aged 11-15. To investigate the relationship between physical activity participation and emotional well-being, regression analyses were run using SPSS Version 27. Results found significant increases in emotional well-being as total weekly physical activity participation increased. In addition, females participated less often in physical activity and displayed significantly lower emotional well-being compared to males, a significant observed decline in well-being occurred in older grade levels compared to younger grade levels, and there were significant increases in emotional well-being as socioeconomic status increased. The current study contributed to an important field of research addressing declines in physical activity participation among youth. Future research can develop adequate school-based interventions to ensure this decline does not continue, as physical activity participation provides important considerations for youth’s current and long-term emotional well-being.
Keywords: physical activity, emotional well-being, health behaviours
Introduction The Canadian guidelines for physical activity state that children and youth should participate in at least 60 minutes of moderate to vigorous physical activity every day of the week. Yet, during the 2013-2014 cycle of the Health Behaviour in Schoolaged Children (HBSC) study, only 1 in 5 participants met these guidelines (Government of Canada, 2016). Considering that adolescence is a period of rapid growth and development, it is important to identify environmental exposures that can affect the development of mental health disorders later in life (Hofstra, van der Ende, & Verhulst, 2002). Regular physical activity is associated with both physical and mental health benefits. Results from the 2013-2014 HBSC cycle found that physical activity relates to better mood, decreased risk of depression and better academic performance (Government of Canada, 2016). This study examined whether physical activity positively relates to adolescents’ emotional well-being. The study also investigated gender, age, and socioeconomic differences in physical activity.
Physical Activity and Emotional Well-being When assessing adolescents’ emotional well-being, it is important to consider both the risks present in their environment and the factors that contribute to positive growth and development (Government of Canada, 2016). Physical activity can be considered a positive factor of particular importance
since it is often a core feature of school curricula, and thus it can be modified to support students’ emotional well-being (Reid et al., 2015). One study found that regular physical activity in young people was associated with improved self-efficacy and self-image, as well as fewer symptoms of depression (Janssen & Leblanc, 2010), while another found that increased levels of physical activity is linked to less depression, anxiety, psychological distress, and emotional disturbances (Ahn & Fedewa, 2011). Despite these benefits of emotional well-being, fewer than half of children meet recommended physical activity participation guidelines, and activity levels appear to decline through adolescence (Troiano et al., 2008). The frequency of participation in physical activity appears to matter for positive outcomes in emotional well-being. Adolescents engaging in fewer hours of physical activity have a higher risk of developing depression and anxiety symptoms than those engaging in regular physical activity (Bélair et al., 2018). In addition, there appears to be a dose-response relationship between years of participation in sports and mental health (Doré et al., 2019). Regular physical activity as an adolescent is important, as being active during this transitional period increases the likelihood of individuals’ regular participation in physical activity in adulthood (Scheerder et al., 2006). Several mechanisms have been suggested as being responsible for the positive effects of physical activity on mental health outcomes. A study by Lubans et al. (2016) observed several neurobiological, psychosocial, and behavioural mechanisms and was able to establish that participation in physical activity improves adolescents’ physical self-perceptions and enhances their self-esteem. Neurobiologically, physical activity enhances cognition and mental health via changes in the structural and functional composition of the brain. Benefits of physical activity include an increase in cell proliferation (Van Praag, 2008), stimulation of growth of new capillaries (Kleim et al., 2002), neurochemicals facilitating the downstream effects on brain structure, function, and cognition (Cotman et al., 2007), as well as effects on the structure of the brain’s cortical and subcortical regions (Chaddock et al., 2010). Psychosocially, emotional well-being is achieved by satisfying basic psychological needs. Physical activity can provide relatedness,
self-efficacy, and perceived competence, as well as positive body image and autonomy (Lubans et al., 2016). Additionally, physical activity can facilitate interactions with one’s environment to improve mood (Thompson Coon et al., 2011). Behaviourally, changes in mental health are mediated by changes in relevant and associated behaviour. Physical activity may improve sleep duration and quality (Stone et al., 2013) and support self-regulation and coping skills (Lubans et al., 2016).
Gender Differences in Physical Activity Participation Canadian girls aged 11 to 17 are consistently less active than boys of the same age (Guthold et al., 2020). Girls less engaged in middle school are also less likely to participate in physical activity by the end of high school (Pate et al., 2007). Therefore, early intervention could facilitate adolescent girls’ regular participation in physical activity. This significant decrease in activity for girls compared to boys appears to be due to various intrapersonal, social, and environmental barriers. An intrapersonal factor that may determine participation is self-efficacy and perceived competence. When participating in physical activity, especially among peers, adolescents want to appear adept at the activity and demonstrate competence. Yungblut et al. (2012) found that adolescent girls sometimes worry about looking good in front of their peers during physical education classes. In the study, several girls reported choosing not to fully participate in physical education classes because they did not want to return to class sweaty since there was no opportunity to shower. This relates to self-image, as adolescent girls are often concerned with maintaining appearances. When adolescent girls do participate in sports, they are more likely to participate in ‘aesthetic’ sports, such as ballet and rhythmic gymnastics. In a study by Schneider et al. (2013), adolescents who participated in these ‘aesthetic’ sports experienced more body dissatisfaction. While regular physical activity is associated with improved self-image in adolescents (Freemanet al., 2011), it can also be associated with low self-image in girls, depending on whether the type of physical activity they pursue is considered an ‘aesthetic’ sport that values appearance.
An important social factor that facilitates participation is positive peer influences. The HBSC 2013-2014 cycle found 11-14% more physically active youth than physically inactive youth in the high friend support group (Government of Canada, 2016). In Yungblut et al.’s (2012) study, girls reported that physical activity was more doable when they had a friend present with them, as this alleviated feelings of dissonance and judgement. Friends offer support and encouragement during stressful situations, such as when girls are tasked to compete against boys. Adolescent girls may also be less privy to peer support during physical activity, as stereotypical ‘girl’ sports tend to be practiced individually (e.g. ballet). In contrast, participation in team sports provides important peer support during adolescence (Rodriguez-Ayllon et al., 2019).
Adolescent girls’ participation in physical activity may also be affected by the environment it is practiced in. Physical education curricula often have a male-oriented approach, which could deter girls from fully participating due to a fear of negative reactions from their male classmates (Vu et al., 2006). In addition, adolescent girls may feel uncomfortable participating in co-educational classes and they may be dissatisfied with physical education uniforms (Allender et al., 2006). Finally, many activities during physical education class are not perceived as being gender appropriate for girls, therefore girls may struggle with impressing their classmates, as looking good for others and physical activity are often seen as incompatible (Dwyer et al., 2006).
Age and Physical Activity Participation During the transitional period from childhood to adolescence, physical activity participation decreases significantly (Lubans et al., 2007). This time period tends to be marked by decreased sports participation and increased non-organized physical activity, which requires more effort to arrange than attending mandatory physical education classes or sports practices (Doré et al., 2016). In addition, technological advances in recent years have contributed to sedentary behaviours, with only 1 in 10 adolescents in Canada adhering to the guidelines of less than 2 hours of screen time per day for recreational purposes (Government
of Canada, 2016). This suggests that not only do adolescents experience a decrease in physical activity participation, but they may also choose to forgo physical activity in favour of sedentary activities that put them at risk for adverse health effects.
equipment. Adolescents are also more likely to be physically active if they belong to groups that value physical activity, such as sports teams (Leventhal & Brooks-Gunn, 2000) thus being unable to access these groups can reduce participation.
As children transition into adolescence, there is normally a decrease in the number of reported physical activities (Aaron et al., 2002). If involved in an organized sport, adolescents may choose to quit or reduce their participation due to increasing levels of competitiveness, an overemphasis on winning, and the stress of high performance. Other factors to consider include monetary costs, risk of injury, as well as a preference for leisure activities (Woods, 2015). Interventions involving all students in a class appear to have the greatest impact on adolescents’ participation in physical activity, therefore schools can be used as an intervention method to increase participation (Ahn & Fedewa, 2011).
Adolescents’ physical activity levels may be influenced by the neighbourhood in which they reside. Lower socioeconomic status neighbourhoods tend to have fewer and worse recreational areas, meaning adolescents need to travel longer distances to access physical activity facilities (Gordon-Larsen et al., 2006). In addition, if parents or guardians feel as if their neighbourhood is unsafe due to higher crime rates, they may not allow or encourage their children to play outdoors with others (Holt et al., 2009). An adolescent from a low socioeconomic background may not only be unable to access recreational areas within proximity of their neighbourhood, but they may also be limited in their opportunities to practice physical activity elsewhere.
During recent decades, adolescents often acquire technology at a much younger age than before, and physical inactivity appears to have increased as a result. Excessive sedentary behaviours are associated with more violent and aggressive behaviours, poor body image, and poor self-esteem (Tremblay et al., 2010). Throughout adolescence, interests tend to shift, such as playing video games with friends instead of playing outside. Physical education contributes significantly to reducing students’ sedentary behaviours (Mayorga-Vega et al., 2018) thus physical activity participation may be superseded by sedentary behaviours when physical education is no longer mandatory.
Socioeconomic Differences in Physical Activity Participation Physical activity levels can differ according to an individual’s socioeconomic status, where individuals with higher socioeconomic status tend to be more physically active than those with lower status (Stalsberg & Pederson, 2010). This appears to be primarily due to a lack of access. Participation in physical activity outside of school is not the norm for many adolescents. Stalsberg & Pederson’s (2010) study found that adolescents in a family with a low socioeconomic status had more difficulties accessing activities that required financial support, such as paying for membership fees and sports
Considering the previous research, physical activity appears to positively relate to adolescents’ emotional well-being. It is important to encourage participation in physical activity as regular participation is associated with both physical and mental health benefits. Participation also varies by gender, age, and socioeconomic status, and these differences influence subsequent physical activity levels. Given this, the study examined the positive influence of physical activity on adolescents’ emotional well-being and how this differed according to gender, age, and socioeconomic status. Methods
Participants The data for this study were derived from the Canadian Health Behaviour in School-aged Children’s (HBSC) 2017-2018 cycle. The HBSC is a cross-national survey administered every four years in 50 countries by the World Health Organization. It observes the health and well-being, social environments, and health behaviours of youth aged 11-15. Data are collected through anonymous self-report questionnaires administered in classroom settings. The Canadian protocol for the survey is coordinated by the Social Program Evaluation Group at Queen’s University and is funded by the Public Health Agency of Canada. A total of 21,750 youth
participated in the 2017-2018 cycle, with no exclusion criteria for this particular study.
Measures Physical Activity Levels and Total Frequency. The experimental variable was assessed by a survey question that asked, “How many hours a week do you usually spend doing the following types of physical activity?” The answers ranged from 1 (none at all) to 6 (about 4 or more hours a week). The measures were a: physical activity during class time at school, b: organized sports that are not part of gym class, c: exercising that is not part of gym class or organized sports, d: activities outdoors that are not part of gym class or organized sports, and e: using active ways like walking or cycling to travel. A composite of these five measures was created to assess the total weekly frequency of physical activity participation. For the descriptive analysis of physical activity and other variables of interest, the variable was dichotomised based on its mean to indicate low physical activity (3.4 hours or less a week) and high physical activity (more than 3.4 hours a week). For the regression analysis of physical activity and other variables of interest, the composite variable total weekly frequency of physical activity was used. Emotional Well-being. The outcome variable was assessed using the WHO-5 Well-Being Index, which asked, “Over the last two weeks, how often have you…?” The answer choices were coded from 1-6, ranging from options 1: all of the time to 6: at no time. The measures were a: felt cheerful and in good spirits, b: felt calm and relaxed, c: felt active and energetic, d: woke up feeling fresh and rested, and e: had your daily life filled with things that interest you. The WHO-5 Well-Being Index has adequate validity to measure current mental well-being (Topp et al., 2015). Control Variables: Gender, Age, and Socioeconomic Status. Gender was assessed as 1 (male), 2 (female) and 3 (neither describes me). In the interest of the research question, the variable was filtered to only males and females. Approximate age was determined based on grade level, considering that the survey was conducted among students aged 11 to 15 (grades 6 through 10). Socioeconomic status, and more specifically, material deprivation, was assessed using the HBSC Family Affluence Scale. This scale measures the presence
of six household items or family activities that symbolise family affluence (number of cars, own bedroom, number of computers, family vacations, dishwasher, number of bathrooms). In the interest of the research question, the summary score was recoded based on its mean for low socioeconomic status (score less than 0.5) and high socioeconomic status (score of 0.5 and higher). The Family Affluence Scale effectively exposes socioeconomic differences in adolescent health (Boudreau & Poulin, 2009).
Data Analysis The research questions were tested via statistical analysis using IBM SPSS Statistics version 27 with a significance level of 5%. Descriptive statistics were reported for the control variables of gender, age, and socioeconomic status. T-test and two-way ANOVA analyses were used to identify initial differences in the control variables. A linear regression model tested the associations between total frequency of physical activity, gender, age, socioeconomic status, and emotional well-being.
Descriptive Results Study participants included 21,750 Canadian adolescents who completed the HBSC survey during the 2017-2018 cycle. Descriptive results are shown in Table 1. In this study, 46.5% of participants were male and 51.3% were female. As seen in Table 2, there were more females than males in the low physical activity group and more males than females in the high physical activity group. The study participants were evenly distributed across five grade levels: 18.7% in grade 6, 20.9% in grade 7, 20.7% in grade 8, 22.2% in grade 9, and 17.4% in grade 10. As seen in Table 2, in grades 6 through 9 there were more participants in the high physical activity group than the low physical activity group. In grade 10, there were more participants in the low physical activity group than in the high physical activity group. For socioeconomic status, based on the scores of the Family Affluence Scale, 57.7% of participants were in the low socioeconomic status group and 42.3% were in the high socioeconomic status group. As seen in Table 2, there were more participants with low socioeconomic status than high
socioeconomic status in the low physical activity group and more participants with high socioeconomic status than low socioeconomic status in the high physical activity group. To identify initial differences between the experimental variable of physical activity levels and other variables of interest (gender, age, and socioeconomic status), t-test and two-way ANOVA analyses were performed. In each analysis run, there was a significant positive main effect of physical activity levels on the outcome variable of emotional well-being (p < .001), which indicates that irrespective of gender, grade level, and socioeconomic status, higher physical activity levels were associated with higher emotional well-being.
-18.23, p = .04, which indicates that higher socioeconomic status is related to higher physical activity. Results from the two-way ANOVA in Table 4 show that there was no significant interaction effect between socioeconomic status and physical activity levels on emotional well-being, F(1, 18447) = .03, p = .86. This indicates that the positive effect of physical activity on emotional well-being did not differ significantly between high and low socioeconomic groups. There was also a significant main effect of socioeconomic status on emotional well-being, F(1, 18447) = 191.25, p < .001, which indicates that irrespective of physical activity levels, higher socioeconomic status was associated with higher emotional well-being.
Results from the t-test in Table 3 show significant differences between males and females in total physical activity, t(20776) = 21.49, p < .001, which means that females participated significantly less in physical activity than males. Results from the two-way ANOVA in Table 4 show that there was no significant interaction effect between gender and physical activity levels on emotional well-being, F(2, 18327) = 1.3, p = .27. This indicates that the effect of physical activity levels on emotional well-being was not different between female and male participants. There was a significant main effect of gender on emotional well-being, F(2, 18327) = 325.81, p < .001, which means that irrespective of physical activity levels, females reported lower emotional well-being than males.
Results from the two-way ANOVA in Table 4 show that there was a significant interaction effect between grade and physical activity levels on emotional well-being, F(6, 18436) = 2.72, p = .01. This indicates that the effect of physical activity levels on emotional well-being was different between grade levels, such that there was a greater difference in emotional well-being between younger adolescents and older adolescents, with older adolescents displaying lower emotional well-being. There was also a significant main effect of grade level on emotional well-being, F(6, 18436) = 156.24, p < .001, which means that irrespective of physical activity levels, grade level influenced emotional well-being. Results from the t-test in Table 3 show significant differences between high and low socioeconomic status groups in total physical activity, t(21206) =
A linear regression analysis was performed to evaluate the prediction of emotional well-being according to physical activity, gender, age, and socioeconomic status. For the first block analysis, the predictor variables gender, age, and low and high socioeconomic status were analyzed. The analysis revealed a statistically significant fit to the data, F(3, 20050) = 733.64, p < .001), where the R2 value suggests that gender, age, and socioeconomic status accounted for 9.9% of the variation in emotional well-being. For the second block analysis, the experimental variable total weekly frequency of physical activity was analyzed. The analysis revealed a statistically significant model, F(1, 20049) = 1377.82, p < .001, where the R2 value suggests that total frequency of physical activity, gender, age, and socioeconomic status accounted for 15.7% of the variation in emotional well-being. Results in Table 5 for the continuous experimental variable physical activity suggest that as the total frequency of physical activity participation becomes higher by one unit (hours per week participating in physical activity), emotional well-being increases by 1.13 units, b = 1.13, t(37.12), p < .001. The subsequent analyses controlled for all variables of interest. The regression coefficient associated with gender suggests that there is a decrease of 1.63 units for emotional well-being in females compared to males, b = -1.63, t(-24.37), p < .001. The regression coefficient associated with grade level suggests that as grade level becomes higher by one unit, emotional well-being decreases by 0.75 units, b = -0.75, t(-29.69), p < .001. The regression coefficient associated with socioeco-
nomic status suggests that as socioeconomic status becomes higher by one unit, emotional well-being increases by .93 units, b = .93, t(13.21), p < .001.
Discussion This study assessed whether physical activity positively relates to adolescents’ emotional well-being. It also assessed whether the outcomes were influenced by differences in gender, age, and socioeconomic status. Consistent with the prediction that physical activity positively relates to adolescents’ emotional well-being, results from the linear regression found significant increases in emotional well-being as total weekly physical activity participation increased. In addition, the sociodemographic characteristics were assessed while controlling for the total frequency of physical activity. For gender, there was significantly lower emotional well-being in females compared to males, with more males in the high physical activity group and more females in the low physical activity group. For age, there was a significant observed decline in well-being in older grade levels compared to younger grade levels, with older grade levels displaying greater returns on their emotional well-being from physical activity. For socioeconomic status, there were significant increases in emotional well-being as socioeconomic status increased. Results from this study indicate that the frequency of physical activity does have positive links to increasing adolescents’ emotional well-being. This result is consistent with previous work in this field, which has found that regular participation in physical activity among young people is associated with a variety of physical and mental health benefits (Government of Canada, 2016). Various psychological hypotheses have been suggested as to why physical activity produces positive effects on emotional well-being (Monteiro-Peluso & Guerra de Andrade, 2005). The distraction hypothesis proposes that diversion from negative stimuli leads to improved mood during and after physical activity (e.g., going for a run after an argument with a friend helps you clear your head). The self-efficacy hypothesis proposes that regular physical activity leads to improved mood and self-confidence since physical activity is perceived as challenging (e.g., feeling proud of your accomplishments in a sport). The social interaction hypothesis proposes that
social relationships experienced during physical activity participation play a beneficial role in improving mental health (e.g., support from a friend during physical education class).
As hypothesized, there were significant differences in physical activity levels and subsequent emotional well-being between genders, ages, and socioeconomic statuses. Regarding gender, adolescent females participated less often in physical activity than their male classmates. This is an especially important finding considering the fact that previous research has found that females with higher scores of physical exercise showed significantly higher self-esteem, which was not true of males (Lagerberg, 2005). In addition, physical activity is positively associated with self-rated health and self-rated mental health in females (Herman, 2015). Given this, it is important to establish interventions that would facilitate adolescent females’ increased participation in physical activity, as it could have important considerations for their emotional and mental health. Regarding age, the study included youth aged 11-15, which is an important age group relating to the decline of physical activity levels. Younger grade levels (grades 6-9) displayed more frequent physical activity participation than a higher grade level (grade 10). This is consistent with previous research, which has found substantial longitudinal changes in moderate-to-vigorous physical activity, decreasing from 5.9 to 4.9 hours a week from early to mid-adolescence (Nelson et al., 2006). When considering participants past the maximum age of the HBSC survey (15), the same study found a decrease in physical activity from 5.1 to 3.5 hours a week from mid-to-late adolescence. This indicates that physical activity levels continue to decline into young adulthood, which can negatively impact emotional well-being. Regarding socioeconomic status, individuals in the low socioeconomic status group had lower participation levels in physical activity than those in the high socioeconomic group. These findings have been seen in previous research (Stalsberg & Pederson, 2010), indicating that socioeconomic status plays a role in participation and should be accounted for when developing intervention programs within schools, as access to activities outside of school may not always be possible. In line with the research that shows physical activity produces beneficial outcomes, implementing
interventions to improve adolescents’ emotional well-being is necessary. This is especially important considering the rising prevalence of mental disorders in youth, as it is believed that one in every four to five youth will develop a mental illness (Zulyniak et al., 2020). As this prevalence of mental illness continues to increase, especially among students, a study by Doré et al. (2016) found that this would be associated with risk behaviours including alcohol and drug abuse, as well as an increase in suicidal ideation. Since experiencing depression and anxiety can hinder academic performance, this can also increase the risk of failure or dropping out of school. Finally, mental disorders in youth have a high risk of chronicity, which may lead to more severe mental disorders later in life, thus interventions among adolescents are imperative. To address these concerns, schools could be an effective environment to promote regular physical activity. However, schools’ existing plans are insufficient. At the time of the 2013-2014 HBSC survey cycle, 49% of the participating schools did not have committees in place to oversee policies and practices concerning physical activity, with 48% not having an improvement plan for the current school year (Government of Canada, 2016). Adolescents’ infrequency of physical activity participation can therefore be attributed in part to the school curriculum. In recent years, schools have been increasingly under pressure to meet testing standards, which often causes less of the school curriculum to be devoted to physical education (Burgeson et al., 2008). As technology usage has also increased, children tend not to compensate for this loss of activity outside of school. While it is essential for schools to focus on academics, which may lead to an inadequate physical education curriculum, physical activity participation can still be modifiable on an individual basis based on a student’s daily life at school or at home (Reid, 2015). Even where there is a lack of physical activity occurring in school curricula, teachers could encourage students to participate more frequently by recruiting them for extracurricular sports teams or suggesting activities occurring in the region. When planning implementations in schools, it is also important to take into consideration the bi-directional relationship between physical activity participation and emotional well-being. According to Rodriguez-Ayllon et al. (2019), young people who already experience greater levels of psychological ill-being subsequently have lower levels of physical
activity participation, which can be explained by a variety of social factors. Enjoyment of physical activity positively influences self-esteem, thus if an adolescent is not interested in or does not enjoy physical activity, they would not reap those benefits (Adachi & Willowby, 2014). Perceived sports competence also plays a mediating role between physical activity participation and self-esteem, with adolescents who do not view themselves as athletic or proficient at a sport being less willing to participate (Wagnsson et al., 2014). Finally, associations between physical activity and life satisfaction may be influenced by the social context (Reigal et al., 2014). For example, an adolescent’s socioeconomic standing could be contributing to their psychological ill-being, in addition to preventing them from participating in physical activity outside of school. In the context of school, student engagement in physical education may be necessary to fully benefit from physical activity’s impact on mental health (Reid, 2016). Physical activity should be perceived as enjoyable for adolescents to sustain their activity levels for longer periods of time. Therefore, finding engaging and enjoyable physical activities is essential to experience the long-term benefits (Lagerberg, 2005).
While the HBSC data used for this study is composed of a large and representative Canadian sample, it has certain limitations in the context of this study’s research questions. The HBSC survey relies exclusively on brief self-report measures provided by the participants. This could influence the results, as adolescents may not be reporting their behaviours accurately. A more comprehensive measure of their health behaviours could be collecting their self-report data in addition to data from their parents or teachers to assess the validity of their claims. Given that this study focused on adolescents’ physical activity participation and subsequent emotional well-being, the large number of measures assessed in the survey may have influenced the determined outcomes in a way that was not considered. For example, the survey assesses measures such as support from family and friends, or experiences of bullying at school, which could also have significant impacts on emotional well-being. While the HBSC data clearly indicates adolescents’ declining physical activity levels and emotional well-being occurring over time, studies tailored specifically to physical activity’s effects on emotional well-being (as seen in previous research) could indicate a more causal link.
While the physical and mental health benefits of regular physical activity are numerous, there still exists the risk of negative outcomes. In some cases, physical activity participation can negatively influence mental health depending on the type of activity being practiced and the context it occurs. Compulsory physical education can cause feelings of emotional distress if an adolescent is not good at sports and is afraid of failure, or if they are afraid to participate for worry that they will be teased (Lagerberg, 2005). In addition, a study by Tobar (2012) found that participation in competitive sports can lead to anxiety, negative mood states, and over-training, due to the pressure that is exerted. Finally, adolescents who participate in ‘aesthetic’ sports that value appearance are more likely to experience body dissatisfaction than adolescents that participate in ‘non-aesthetic’ sports (Schneider, 2013). This suggests that not all types of physical activity will lead to the same levels of satisfaction in physical appearance, which can play an important role in subsequent emotional well-being. Considering these potential risks, preventive measures should be taken within these vulnerable areas to ensure that adolescents are fully benefiting from physical activity participation.
Conclusion This study observed Canadian adolescents’ physical activity levels and the subsequent influence the frequency of participation had on their emotional well-being. The sociodemographic variables of gender, age, and socioeconomic status were also controlled to examine differences in participation and emotional well-being. Previous research has observed that physical activity participation in adolescents is insufficient and continues to decline. This trend has important implications for emotional well-being, as physical activity is considered an important determinant in both physical and mental health. Taking into consideration these results, interventions should be implemented within schools, as this is a crucial environment for the promotion of participation in physical activity. The development of these interventions must take into consideration the difficulties that various individuals face in accessing physical activity due to gender, age, and socioeconomic differences. They should also consider some of the limitations as to why physical activity levels are declining, such as inadequate physical education curricula and increased focus on performance and skill rather than enjoy-
ment. This is an important topic for future research to continue in, as physical activity levels in adolescents have been declining over time and will likely continue to do so. It is imperative that adequate measures be put in place to ensure this decline does not continue, as physical activity participation provides important considerations for adolescents’ current and long-term emotional well-being.
Statement of Contribution
The following paper was made possible by the Health Behaviour in School-aged Children study’s 2017-2018 Canadian survey cycle database, in which access to it was provided by Dr. Frank Elgar, who is a member of the Canadian research team. The HBSC study is administered in 50 World Health Organization member countries every 4 years at selected schools to children aged 11-15. Due to the multi-disciplinary approach involved in the survey, HBSC findings can be used by researchers and policy-makers to monitor young people’s health, understand the social determinants of health, and determine effective health improvement interventions. In using the HBSC database, data collection did not occur in the context of this study. Assistance with data analysis was provided by Samira Feizi (M.A.), a current graduate student at McGill, as well as by Dr. Frank Elgar.
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Table 1 Descriptive Statistics of Participants’ Emotional Well-being Sociodemographic Characteristics
Low Physical Activity
High Physical Activity
Mean Std. Deviation
Low Socioeconomic Status
High Socioeconomic Status
Note. N = 18, 333 for gender, 18, 450 for age (grade), and 18, 451 for socioeconomic status.
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Table 2 Cross-tabulation of Participants’ Physical Activity Levels Sociodemographic Characteristics
Low Physical Activity
High Physical Activity
AGE (GRADE) Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine and Science in Sports and Exercise, 40(1), 181–188. doi.org/10.1249/ mss.0b013e31815a51b3 van Praag, H. (2008). Neurogenesis and exercise: Past and future directions. Neuromolecular Medicine, 10(2), 128–140. doi.org/10.1007/s12017-008-8028-z Vu, M. B., Murrie, D., Gonzalez, V., & Jobe, J. B. (2006) Listening to girls and boys talk about girls’ physical activity behaviours. Health Education Behaviour, 33, 81-96. doi. org/10.1177/1090198105282443
Low Socioeconomic Status
High Socioeconomic Status
Note. N = 21, 586 for gender, 21, 749 for age (grade), and 21, 750 for socioeconomic status.
Table 3 Independent Samples Test of Sociodemographic Differences in Total Physical Activity Sociodemographic Characteristics
Low Socioeconomic Status
High Socioeconomic Status
Table 4 Tests of Between-Subject Effects of Physical Activity Levels According to Various Sociodemographic Factors
LOW/HIGH PHYSICAL ACTIVITY * GENDER
Low/High Physical Activity
Low/High Physical Activity
Low/High Physical Activity
Low/High Socioeconomic Status
LOW/HIGH PHYSICAL ACTIVITY * AGE (GRADE)
LOW/HIGH PHYSICAL ACTIVITY * LOW/HIGH SOCIOECONOMIC STATUS
Table 5 Coefficients of Emotional Well-being According to Gender, Age (Grade), Socioeconomic Status and Total Physical Activity B
95% Confidence Interval Lower Bound Upper Bound
Total Physical Activity
The Relationship between Mindfulness Facets and Aggression in Young Women
Danielle Fuchs Department of Educational and Counselling Psychology, McGill University PSYC 450 D1/D2: Research Project & Seminar Supervisor: Dr. Bärbel Knäuper Co-Supervisors: Dr. Bassam Khoury & Matthew Fleischmann April 29, 2021
Abstract Although previous research has explored the relationship between mindfulness facets and aggression in children and adults, the research examining aggression in women is limited. Many young adult women exhibit aggression, which may interfere with their lives and hurt their loved ones. Mindfulness has been studied as a method to mitigate the effects of aggression, and it correlates negatively with hostility, anger, and aggressive behaviour. Additionally, studies have found a relationship between self-compassion and aggression. This study aimed to understand the relationship between mindfulness facets, self-compassion, and aggression in a sample of young adult women. Participants (n =125) completed the Five Facet Mindfulness Questionnaire, the Self-Compassion Scale, and the Buss and Perry Aggression Questionnaire. The results of the study found a statistically significant negative correlation between aggression and the mindfulness facets describing, acting with awareness, non-judging, and non-reactivity. The results also found a statistically significant negative correlation between aggression and self-compassion. There was not a statistically significant negative correlation between aggression and the mindfulness facet of observing. Furthermore, self-compassion explained variance in aggression above and beyond individuals’ mindfulness scores. Results indicate that future research on mindfulness-based interventions for aggression should focus primarily on the facets of non-judging, acting with awareness, and non-reactivity. Additionally, future research should explore the possible role of interventions that enhance self-compassion to reduce aggression in young women.
Keywords: aggression, mindfulness, self-compassion, women, young adults
Aggression Aggression can be defined as behaviours that are intended to physically or mentally harm another person (Anderson, 2012). Humans have naturally developed aggressive behaviours to protect their partners, offspring, resources, and themselves (Anderson, 2012). Aggression is present in many types of externalizing disorders (disorders that are directed toward one’s environment, rather than towards oneself, such as oppositional defiant disorder, conduct disorder, attention deficit hyperactivity disorder), and has been found to correlate with symptoms of anxiety and depression (Dugré, 2020; Poore, 2020). Aggression interferes with people’s lives and hurts their loved ones (Polanin, 2020; Pugh, 2009). However, despite ample research on aggression in samples of boys and adult men (e.g., Achterberg et al., 2020; Andrews, 2018; Franco et al., 2016; Stephens et al., 2020; Yusainy & Lawrence, 2015), there remains a paucity of aggression research on women (Pugh, 2009). This gap in research is significant because men and women present differences in their tendencies toward aggressive behaviours, so further research is necessary to understand aggression in women (Björkqvist, 2018; Denson et al., 2018; Wiggins, 1991). Previous research has attempted to determine whether men and women are equally aggressive and possible gender and sex differences in aggression (e.g., Campbell, 2013; Hyde, 1990). Men are more aggressive than women on average, but this
difference presents largely during childhood and decreases with age (Hyde, 1990). Furthermore, men are more likely to display overt or physical aggression, such as hitting, while women tend to display covert or verbal aggression, such as spreading rumors (Björkqvist, 2018; Pugh, 2009). Many psychologists believe men evolved to be more aggressive to compete for a higher social status, proving their superior strength, and, thus, increase their chances of finding a mate and of reproduction (Björkqvist, 2018; Wiggins, 1991). Others believe that, traditionally, as an efficient method of dividing labour, men held more physical roles, while women held more nurturing roles (Krahé, 2021; Wiggins, 1991). However, some criticize both these evolutionary and the social hypotheses for lacking sufficient evidence (Wiggins, 1991).
As aggression differs significantly in men and women, it is important to explore more on young women’s aggression. Due to the harm associated with aggression (Polanin, 2020; Pugh, 2009), research has begun to explore some positive coping strategies, such as gratitude (Deng, 2019), close friendships (Andrews, 2018), or cooperation with others (Velez, 2014), that correlate with reduced aggression. Knowing how coping strategies may correlate with reduced aggression can inform research on interventions for minimizing aggression. Mindfulness is another coping strategy that has been explored for its positive correlation with reduced aggression (e.g., Eisenlohr-Moul, 2016; Franco et al., 2016; Gao, 2016; Garofalo, 2020). Previous studies on the relationships between mindfulness, self-compassion, and aggression have had more men than women in their samples (Gao, 2016; Stephens et al., 2020) or no women at all (Garofalo, 2020).Therefore, research on the relationship between mindfulness and young women’s aggression is necessary, as the literature is limited, and it is important to find effective coping strategies to mitigate the harm that comes with aggression, including the intergenerational cycle of aggression in women (Garofalo, 2020; Polanin, 2020; Pugh, 2009; Stephens et al., 2020).
Mindfulness Mindfulness can be defined as the awareness that results from intentionally and non-judgmentally focusing on the present moment (Kabat-Zinn, 1990). Trait mindfulness is often conceptualised
through five facets: observing (attending to one’s feelings), describing (naming those feelings), acting with awareness (focusing on what is happening in the moment), non-judging (not judging one’s own thoughts and feelings), and non-reactivity (not reacting to those thoughts and feelings), and is often measured using the Five Facet Mindfulness Questionnaire (FFMQ; Baer, 2004, 2006). Mindfulness has been found to have an inverse relationship with three of the four components of aggression measured by the Buss and Perry Aggression Questionnaire (Buss, 1992), (Eisenlohr-Moul, 2016; Jennings, 2013). Additionally, meta-analyses and systematic reviews have found a positive relationship between trait mindfulness and decreased negative affect, which is the experience of negative emotions (Enkema, 2020), rumination, which is the continuous worrying and thinking about an event or other stimulus (Enkema, 2020), depressive symptoms (Aster, 2016; Bajaj, 2016; Enkema, 2020; Pearson, 2015), symptoms of anxiety (Bajaj, 2016; MacDonald, 2020; Pearson, 2015), hostility (Jennings, 2013), stress (MacDonald, 2020), drug and alcohol use (Single, 2019), anger (Eisenlohr-Moul, 2016), and especially, aggressive behaviour (Eisenlohr-Moul, 2016). Specific facets of mindfulness have been shown to relate to specific mental health outcomes. For example, Single et al. (2019) found that undergraduate students who scored higher on acting with awareness, non-judging, and non-reactivity were less likely to develop problems with drug and alcohol use and had fewer depression and anxiety symptoms. Tangney (2017) found that non-judgment of the self had a significant negative correlation with criminogenic thinking (thinking that rationalizes deviant behaviour) among both inmates and college students. Additionally, research has found an especially large effect size for the relationship between non-judgment and aggression and acting with awareness and aggression, compared to other facets of mindfulness (e.g., Peters et al., 2015; Velotti et al., 2016a). The inverse relationships between trait mindfulness and negative affect (Enkema, 2020; Poore, 2020), emotion regulation (Garofalo, 2020; Hölzel, 2011), hostility (Buss, 1992; Jennings, 2013), drug and alcohol use (Gold, 2020; Single, 2019), anger (Buss, 1992; Eisenlohr-Moul, 2016), and aggres-
sive behaviour (Eisenlohr-Moul, 2016), have been established. Furthermore, several studies have found an inverse relationship between mindfulness and behaviors related to aggression, such as substance use, hostility, and anger rumination, in undergraduate students (e.g., Gao, 2016; Pearson, 2015; Single, 2019). As previous research has demonstrated a negative association between mindfulness and aggression in undergraduate samples and in a sample that included undergraduate women (Gao, 2016), it is likely that mindfulness will also correlate with reduced aggression in this sample of young women.
2009; Velotti et al., 2016b). Moreover, self-compassion has an inverse relationship with violent criminality (Morley, 2015), anger (Fresnics, 2016; Neff, 2009), and aggressive behaviour (Fresnics, 2016; Morley, 2015; Sommerfeld, 2020). Research on the relationship between self-compassion and individuals’ mental health has also shown similar results to those of research on mindfulness, such as improved well-being (Henriksson, 2016; Zessin, 2015), higher positive affect (Zessin, 2015), and positive mental health outcomes (Nevv, 2003b).
Research should explore which components of mindfulness, such as the five facets measured by the FFMQ, specifically correlate with aggression in women (Barnard, 2011; Morley, 2015; Neff, 2003b). When researching mindfulness, it is important to also measure participants’ self-compassion. Although it is a separate construct from mindfulness itself, self-compassion is connected to mindfulness practices (Barnard, 2011) and has been found to inversely correlate with aggressive behaviour (Morley, 2015; Sommerfeld, 2020). As such, it may be beneficial to investigate whether self-compassion explains variance in aggression above and beyond trait mindfulness.
Investigating the relationships between mindfulness and aggression and self-compassion and aggression in a sample of young women will inform further research on the use of mindfulness to reduce aggression in young women. As such, this study aimed to understand the relationship between mindfulness facets, self-compassion, and aggression in young women.
Self-Compassion Self-compassion has been defined as caring for and having compassion for oneself without having any less compassion for others (Neff, 2003a). Self-compassion also entails one’s ability to approach psychological discomfort with awareness, openness, and acceptance, rather than avoidance (Neff, 2003a). Self-compassion is typically measured using the Self-Compassion Scale (Neff, 2003a). Research has linked self-compassion to emotion regulation (Baer, 2003; Hölzel, 2011) and frequency of meditation (Bodhi, 2011; Neff, 2003a). Additionally, mindfulness is one of three core components of self-compassion (Barnard, 2011). Researchers have established that it is valuable to include self-compassion in studies of aggression (e.g., Morley, 2015). Previous research has established a relationship between self-compassion and aggression (Morley, 2015; Sommerfeld, 2020) and has found that constructs related to self-compassion, such as self-esteem, correlate with aggression (Neff,
Consistent with past research, we hypothesized: H1. Non-judging of inner experience will be negatively associated with aggression for young women. H2. Acting with awareness will be negatively associated with aggression for young women. H3. Self-compassion will be negatively associated with aggression for young women. H4. Self-compassion scores will explain variance in aggression for young women even when controlling for their trait mindfulness, indicating that self-compassion alone further explains variance in aggression for young women. Methods
Participants A sample of 132 participants aged 18 to 25 were recruited through flyers posted on Instagram and Facebook. Two participants were excluded because they were outside the age range of 18 to 25, and four participants were excluded because they indicated that they were not women. Thus, the final sample size was 126 young women. All participants were entered into a raffle to win one of five $30 Starbucks gift cards as compensation. Eighty-one percent of the participants were White, and the rest indicated that they were Asian, Hispanic or Latinx, Multi-racial, or other. None
identified as Black or Indigenous. More than half of the participants were from the United States and approximately one third were from Canada. The rest of the participants were from fourteen other countries, located in Latin America, Africa, the Middle East, Southeast Asia, and Eastern, Western, and Central Europe. Participant demographics can be found in Table 1.
Procedure This research project was submitted for approval and accepted by the McGill Research Ethics Board. After giving us their informed consent, participants completed a demographics questionnaire and the below listed measures of mindfulness, self-compassion, and aggression. These were administered on the online platform LimeSurvey.
Five Facet Mindfulness Questionnaire The Five Facet Mindfulness Questionnaire (FFMQ; Baer, 2004), a 39-item self-report measure, was used to measure participants’ trait mindfulness. This scale measures the five facets of mindfulness: non-judging of inner experience, observing, describing, acting with awareness, and non-reactivity to inner experience. Participants were asked to indicate the extent to which each item was true for them, on a scale from 1 (never or very rarely true) to 5 (very often or always true). Sample items include: “It is hard for me to find the words to describe what I am thinking” and “I make judgments about whether my thoughts are good or bad.” The FFMQ has been shown to be both valid and reliable for all of the facets that it measures in a sample that included 263 women and 179 participants that were students, out of a total of 349 participants (Christopher, 2012). In the present study, the facets showed strong internal consistency, ranging from α = .764 for the facet of observing to α = .939 for the facet of non-judging (see Table 2). Self-Compassion Self-compassion was measured using the Self-Compassion Scale (Neff, 2003a), a 26-item self-report measure. Participants were asked to indicate the extent to which each item was true for them, on a scale from 1 (almost never) to 5 (almost always). Sample items include: “I try to be loving towards myself when I’m feeling emotional pain” and “I try to see my failings as part of the
human condition.” Research has shown a significant correlation between self-compassion and greater life satisfaction (Neff, 2003a). The scale has been found to be valid and reliable in at least two samples of participants aged 18-30 (mean ages 20.60 and 20.49), and approximately two-thirds of the participants in these samples were women (Neff, 2020). The Self-Compassion Scale showed strong internal consistency in the present study (α=.967). Buss and Perry Aggression Questionnaire The Buss and Perry Aggression Questionnaire (Buss, 1992), a 29-item self-report measure of physical aggression, verbal aggression, anger, and hostility, was used to measure participants’ aggression. Participants were asked to indicate the extent to which each item was true for them, on a scale from 1 (extremely uncharacteristic of me) to 5 (extremely characteristic of me). Sample items include: “When frustrated, I let my irritation show” and “I get into fights a little more than the average person.” The Buss and Perry Aggression Questionnaire has been found to be both a reliable and valid measure of aggression in a sample of 591 students (Habibi, 2017). The Buss and Perry Aggression Questionnaire showed strong internal consistency in the present study (α=.902).
Statistical Analysis The participants’ responses to the FFMQ were separated by facet of mindfulness and each facet was scored individually. Their responses to the Self-Compassion Scale and the Buss and Perry Aggression Questionnaire were also scored. Missing values were replaced by the mean for their respective questions. The outcome variable of aggression was found to have a normal distribution. The data were cleaned for univariate outliers and multivariate outliers, using SPSS (version 27). Five univariate outliers were identified and were replaced with the closest value that was no longer an outlier. One multivariate outlier was identified and was removed from the dataset. A hierarchical multiple regression was conducted using SPSS (version 27) to examine the relationships between each of the five facets of mindfulness and self-compassion with the outcome of aggression, as measured by the Buss and Perry Aggression Questionnaire.
Results We hypothesized that the mindfulness facets of non-judging and awareness would show a statistically significant negative correlation with aggression, which was supported by the data. Additionally, we hypothesized that self-compassion would show a statistically significant negative correlation with aggression and that self-compassion would explain variance in aggression when controlling for all mindfulness facets, which was further supported by the data. Overall, 21.5% of the variance in aggression was explained by all five facets of mindfulness and self-compassion. The results for the correlations can be found in Table 3.
defined as people from ages 18 to 25 who identify as women.
Relationships Between Mindfulness Facets and Aggression
These findings are consistent with previous research that has demonstrated inverse correlations of overall mindfulness scores with aggression and various specific behaviours and attitudes related to aggression (e.g. hostility, physical aggression; Baer, 2004, 2006; Buss, 1992; Eisenlohr-Moul, 2016; Jennings, 2013). Additionally, consistent with previous research, this study found that the specific facets of non-judgment of inner experience and acting with awareness had significant negative correlations with aggression (e.g., Peters et al., 2015; Velotti et al., 2016a).
All five facets of mindfulness were found to have statistically significant negative correlations with aggression, except for that of observing (r=.029; p=.373). Of the facets of mindfulness, the largest correlations were found for the facets of non-judgment of inner experience (r=-.341; p=.000) and acting with awareness (r=-.321; p=.000). Describing (r=-.249; p=.003) and non-reactivity to inner experience (r=-.281; p=.001) also had statistically significant negative correlations with aggression.
Relationship Between Self-Compassion and Aggression Self-compassion was found to have a statistically significant negative correlation with aggression (r=.397; p=.000). The change in the value of R square when adding in self-compassion as a factor was .048, so self-compassion alone explained 4.8% of the variance in aggression, beyond the variance that was explained by the mindfulness facets. This was a statistically significant change in R square (p=.008), so introducing self-compassion as a variable led to a significant increase in predictive power compared to that of only the mindfulness facets (see Table 4). Discussion This study explored how the five mindfulness facets (observing, describing, acting with awareness, non-judging of inner experience, and non-reactivity to inner experience) and self-compassion correlate with aggression in young women, which was
Mindfulness Consistent with our hypotheses, the mindfulness facets of non-judging of inner experience and acting with awareness were found to have statistically significant negative correlations with aggression. Two other facets of mindfulness (describing and non-reactivity) were found to significantly inversely correlate with aggression. Observing, the final facet of mindfulness, was not found to have a statistically significant correlation with aggression.
These results are important because they provide further insight into protective factors for aggression in young women, who have previously been underrepresented in research on aggression (Pugh, 2009). Furthermore, the finding that the mindfulness facet of observing does not correlate with aggression indicates that observing (attending to one’s feelings) may not be important in predicting levels of aggression (Baer, 2004, 2006).
Self-Compassion Self-compassion was not only found to statistically significantly correlate with aggression scores but was also found to explain variance in aggression for the sample above and beyond individuals’ mindfulness scores. This finding is consistent with previous research on the inverse correlation between self-compassion and aggression (Neff, 2003a). The finding that self-compassion explained variance in aggression above and beyond the participants’ mindfulness scores is particularly notable, as the research on the relationship between self-compassion and aggression is scant (Fresnics, 2016; Morley, 2015; Sommerfeld, 2020).
Our results suggest that young women in the sample who tend to focus on what is happening in the present moment, accept their own thoughts and feelings, and are caring and compassionate toward themselves report, on average, lower levels of hostility, anger, physical aggression, and verbal aggression.
Strengths and Limitations
Our study has a number of limitations that need to be addressed. Notably, our sample lacked racial diversity, with no participants indicating that they were African American/Black or Indigenous and the majority indicating that they were White/Caucasian. Future research should aim to have more representative samples with better racial diversity. There were time constraints, as the study had to be conducted over a period of six months, leaving limited time for participant recruitment. Although the sample size was sufficient for the statistical analyses, there could have been a larger sample had there been more time. Additionally, this study only included participants between the ages of 18 and 25 due to a lack of sufficient access to other age groups, so the results cannot generalize to groups other than young adults. Similar studies should be conducted with more representation of people of varying ages. Furthermore, as this study only established correlations between the variables, there may be extraneous confounding variables, we cannot infer the direction of the relationships between the variables, and we cannot establish causation. Future research should consider using rigorous designs.
Conclusion This study demonstrated that self-compassion and the mindfulness facets of describing, acting with awareness, non-judging of inner experience, and non-reactivity to inner experience all correlate negatively with aggression in a sample of young women. Furthermore, the results indicate that self-compassion explains variance in aggression above and beyond the influence of mindfulness facets. If these results generalize to the population of young women, future research on mindfulness-based interventions for aggressive young women should emphasize these mindfulness facets, especially those of non-judgment of inner
experience and acting with awareness, as they showed the strongest correlations with aggression. Additionally, if the negative correlation found between self-compassion and aggression generalizes to the population, future research should emphasize increasing self-compassion in interventions for aggressive young women. A follow-up study should also examine potential mechanisms of the inverse correlations of mindfulness and self-compassion with aggression.
Further research is needed to replicate these results and demonstrate that they generalize, especially to Black and Indigenous young women. However, this study provided insight into how mindfulness facets and self-compassion relate to aggression in young women and can be used to inform future research on interventions for aggression in this demographic.
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The Effect of Perfectionism on the Experience of Anxiety During the COVID-19 Pandemic
Glory Chima Department of Psychology, McGill University PSYC 380 D2: Honours Research Project Seminar Dr. Sarah Racine
Abstract Research assessing the psychological impact of the COVID-19 pandemic and the risk factors for mental health problems during the pandemic has been rapidly increasing. Despite this, the trait of perfectionism has yet to be explored as a potential risk factor. The current study aimed to understand the impact of the COVID-19 pandemic on anxiety in young adults, utilizing self-determination theory to understand how perfectionism may impact symptoms of anxiety during the pandemic. We examined the trajectory of the anxiety of participants in two three-wave longitudinal studies conducted in the 2018-2019 and 2019-2020 academic years, measuring levels of perfectionism, anxiety, and need frustration of the need for competence at baseline using brief questionnaires. The results indicate that the trajectory of anxiety in the pre-pandemic (2018-2019) year was not significantly different from that of the pandemic (2019-2020) year. In both academic years, self-critical perfectionism predicted an increase in anxiety over the year. However, this was not found for personal standards perfectionism. Finally, although need frustration in March predicted anxiety in May, perfectionism at baseline did not predict need frustration in March. Thus, need frustration did not medicate the relationship between perfectionism and anxiety. This study highlights a potential risk factor for decreased mental wellbeing as a result of the COVID-19 pandemic. This area of research is critical in order to understand which variables are related to psychological distress and how such factors may affect one’s ability to cope during unprecedented times.
Keywords: perfectionism, anxiety, coronavirus, COVID-19, pandemic, self-determination theory
The Effect of Perfectionism on the Experience of Anxiety During the COVID-19 Pandemic The COVID-19 pandemic has had a pronounced negative impact on the health and financial prospects of the public. According to the World Health Organization (2020), as of August 5, 2020, there have been over 18 million cases of COVID-19 and nearly 800,000 deaths worldwide (World Health Organization [WHO], 2020). From the time the pandemic was declared on March 5, 2020, unemployment rates have skyrocketed; in the United States alone, over 30 million adults have applied for unemployment benefits (Crayne, 2020). In Canada, one-in-ten working-age individuals have either lost their jobs or worked less than half their usual hours as of April 9, 2020 (McIntyre & Lee, 2020). The effect of the COVID-19 pandemic on the economy has been predicted to continue past the peak of the pandemic, and researchers suggest that it may take many years before employment opportunities become available (Berman, 2020; Martin, 2020). In an attempt to control the spread of the COVID-19 virus, several countries have imposed measures to limit social interaction, including the implementation of curfews, the closing of schools and businesses, and social distancing and stay-athome orders (Khatatbeh, 2020). As a result of these measures, the public is experiencing a new lifestyle that entails high levels of uncertainty and extraordinary levels of social isolation (McElroy et al., 2020).
Well-being during the COVID-19 pandemic
Not surprisingly, these significant changes have greatly impacted the mental wellbeing of the public. Research indicates that, throughout the pandemic, levels of negative emotions such as anger have increased, while positive emotions and life satisfaction have decreased (Sher, 2020). Furthermore, the rapid increase in unemployment rates has been predicted to result in over three thousand excess suicides between 2020 and 2021 (McIntyre & Lee, 2020). Other research suggests that the COVID-19 pandemic is associated with multiple mental health issues, including sleep disturbances, depression, and anxiety (Sher, 2020). In a study assessing the specific forms of anxiety experienced during the pandemic, McElroy et al. (2020) captured two primary forms of pandemic-related anxiety: Disease Anxiety, which refers to fears of catching and/or transmitting the COVID-19 virus, and Consequence Anxiety, which refers to the fear of the impact of the pandemic on different factors, such as on the economy. Researchers have also aimed to identify different risk factors and predictors of different mental health problems, such as anxiety and depression during the pandemic (Al Omari et al., 2020; Horesh et al., 2021). In one study conducted during the last two weeks of March 2020, just after a pandemic had been declared, researchers had participants complete self-report questionnaires assessing perceived stress, anxiety, and quality of life (Horesh et al., 2020). There were also various questions about having to quarantine, pre-existing health problems, and worries related to the COVID-19 virus. Findings indicated that female gender, younger age, corona-related loneliness, and pre-existing chronic illness were all associated with higher levels of psychological distress and lower levels of quality of life during the COVID-19 pandemic. Another study produced similar findings, with significant predictors of stress, anxiety, and depression during the COVID-19 pandemic including being female, being in contact with a friend and/or family member with mental health problems, being quarantined for 14 days, and using the internet (Al Omari et al., 2020). Finally, one study looking at risk factors for suicidal ideation and depression during the COVID-19 pandemic found that risk factors included being female, being of younger age, being a cigarette smoker, having co-morbid diseases, and having
insomnia symptoms (Mamun et al., 2021). Putting these findings together, research not only indicates that the COVID-19 pandemic is associated with an increase in mental health problems but also that certain risk factors increase the occurrence of mental health issues.
Perfectionism Although there is a growing body of research assessing the psychological impact of the COVID-19 pandemic as well as certain risk factors associated with mental health issues during the pandemic, a potential risk factor that has received little scientific attention throughout the pandemic is the personality trait of perfectionism. This is notable given the expansive body of research indicating that perfectionism is associated with various mental health issues (Dunkley et al., 2003; Egan et al., 2011; Moore et al., 2020). Perfectionism is defined as the tendency to believe that anything less than perfect is unacceptable (Merriam-Webster, 2015). Previous research indicates that there are two forms of perfectionism and that while one is related to negative outcomes, the other is related to both positive and negative ones (Stoeber & Otto, 2006). Personal standards perfectionism, which is the more positive form of perfectionism, involves “the setting of high standards and goals for oneself” (Dunkley & Zuroff, 2003, p. 234) and has been associated with a mix of positive and negative outcomes. On the positive end, personal standards perfectionism is associated with positive affect (Chang et al., 2004). Furthermore, research suggests that, although individuals high in personal standards perfectionism tend to generate stress in their lives by focusing on the negative aspects of events, this negative impact might be counterbalanced by their tendency to engage in active, problem-focused coping (Dunkley et al., 2000). On the negative end, personal standards perfectionism has been associated with anxiety, negative affect, and increased levels of stress (Dunkley et al., 2003; Hill et al., 2004). In contrast to personal standards perfectionism, self-critical perfectionism, which is the negative form, involves “constant and harsh self-scrutiny, overly critical evaluations of one’s own behaviour, an inability to derive satisfaction from successful performance, and chronic concerns about others’ criticism and expectations” (Dunkley & Zuroff,
2003, p. 234). This form of perfectionism is consistently associated with multiple negative outcomes, including depressive and anxious symptoms, suicidal ideation, and negative affect and avoidant coping (Chang et al., 2004; Dunkley et al., 2003; Dunkley et al., 2020; Moore et al., 2020). Research has also examined the influence of self-critical perfectionism on stress and coping. In one study, Dunkley et al. (2003) found that, compared to individuals low in self-critical perfectionism, individuals high in self-critical perfectionism were more emotionally reactive to stressors associated with a loss of control, the potential for failure, and criticism from others. This relationship between self-critical perfectionism and emotional reactivity was mediated by multiple maladaptive, avoidant coping strategies such as disengagement and denial. Furthermore, the relationship between self-critical perfectionism and avoidant coping was mediated by self-blame and low perceived efficacy. Another study found that individuals high in self-critical perfectionism were less likely to engage in disclosure when under high levels of stress, a time when disclosure is most beneficial, compared to those low in self-critical perfectionism (Richardson & Rice, 2015). Finally, one study found that participants with high levels of self-critical perfectionism and lower perceived control had higher levels of depressive and anxious symptoms four years later (Dunkley et al., 2003). Taken together, research not only indicates self-critical perfectionism to be associated with symptoms of psychopathology but also to be associated with maladaptive tendencies, such as avoidant coping, when faced with distress. The aforementioned findings have implications for how individuals high in self-critical and personal standards perfectionism may be impacted during the pandemic. For instance, the current COVID-19 pandemic is a time of great uncertainty and uncontrollability for many individuals and, as aforementioned, research indicates that individuals with high levels of self-critical perfectionism and lower perceived control tend to experience higher levels of depressive and anxious symptoms. Furthermore, these times of uncertainty and uncontrollability are likely to be experienced as highly stressful for most individuals and, as previously indicated, individuals high in personal standards perfectionism tend to experience increased levels of stress. Consequently, linking these previous findings on perfectionism with the current situation that has
arisen due to the COVID-19 pandemic, the mental health of individuals high in self-critical and personal standards perfectionism may be disproportionately impacted during the current pandemic.
Self-Determination Theory Self-determination theory (SDT) may offer a suitable theoretical framework through which we can understand how perfectionism may play a role in the experience of psychological distress during the COVID-19 pandemic, as this theory highlights different factors that may contribute to or stand in the way of one’s wellbeing. Self-determination theory argues that humans have three fundamental needs: the need for autonomy, the need for competence, and the need for relatedness (Deci & Ryan, 2000). Autonomy refers to feelings of volition and self-authorship, in contrast to feelings of being externally controlled (Yang et al., 2018). Competence is composed of succeeding at tasks, attaining desired outcomes, and feelings of effectiveness and mastery, as opposed to feelings of failure, incompetence or inadequacy. Relatedness is characterized by a sense of connectedness and feelings of belongingness with significant others (Kranabetter & Niessen, 2019). Research suggests that having these three needs met is associated with increased well-being, while having these needs thwarted is associated with decreased well-being (Campbell et al., 2018; Deci & Ryan, 2000; Sheldon et al., 2010; Wei et al., 2005). For example, in a study assessing whether the satisfaction and frustration of the three aforementioned psychological needs would relate, respectively, to adolescents’ psychological wellbeing and maladjustment, Rodríguez‐Meirinhos et al. (2019) found that need satisfaction contributed uniquely to well-being, while need frustration contributed uniquely to psychological adjustment issues. This relationship was invariant across age, gender, and socioeconomic status (SES) groups. Another study by Yang et al. (2018) assessing international students studying in the United States found that the relationship between self-determined motivation to study abroad and the experience of culture shock and subjective well-being was mediated by the satisfaction of the three psychological needs. Specifically, greater self-determined motivation to study abroad was associated with greater satisfaction of the three psychological needs, which was related to increased subjective well-being and decreased culture shock. Several
other studies have also found associations between need satisfaction and multiple adaptive psychological outcomes such as feeling more energetic, greater life satisfaction, and greater positive affect (Chen et al., 2015; (DeHaan et al., 2016; Sheldon et al., 2001).
Previous studies also demonstrate the value of analyzing the relationship between perfectionism and mental health indicators through SDT. For example, in a study assessing the relationship between perfectionism and goal progress, Moore et al. (2018) found that self-critical and personal standards perfectionism were differentially related to goal progress. Furthermore, both the relationship between self-critical perfectionism and goal progress and the relationship between personal standards perfectionism and goal progress was mediated by goal-related autonomous motivation. A follow-up study (Moore et al., 2020) aiming to replicate these findings and to assess the impact of perfectionism on depressive symptoms found that self-critical perfectionism was associated with depressive symptoms. This relationship was partly mediated by goal-related autonomous motivation. Finally, one study by Campbell et al. (2018) found that self-critical perfectionism was associated with eating disorder symptoms and depressive symptoms, and this relationship was mediated by need frustration of the three psychological needs. These studies, particularly the latter two findings, suggest that analyzing the relationship between perfectionism and mental health outcomes through the lens of SDT may serve as a helpful way to understand the mediators between these two variables. Research indicates that individuals high in perfectionism have a strong focus on goal striving (Nepon et al., 2016). This elevated focus on goal striving is comparable to the need for competence as outlined under SDT, as both concepts focus on goal striving and achievement. The need for competence is met when individuals succeed at important tasks and attain desired outcomes, while this need is frustrated by experiences of failure and inadequacy in completing daily tasks and obtaining desired outcomes (Rodríguez-Meirinhos et al., 2020). Placing these findings in the context of the current pandemic, the changes and restrictions implemented because of the COVID-19 pandemic, such as increases in unemployment rates and
the implementation of preventive measures such as stay-at-home orders, have led to decreased opportunities for individuals to focus on achieving goals. As a result, the COVID-19 pandemic may have thwarted the need for competence for many individuals, but especially individuals high in the trait of perfectionism. Given previous research indicating need frustration to be associated with compromised well-being, it is also highly possible that the COVID-19 pandemic may negatively impact the mental health of individuals high in perfectionism, in part by thwarting these individuals’ need for competence. Present Investigation The present study aimed to understand the impact of the COVID-19 pandemic on anxiety in young adults. Specifically, we utilized SDT to understand how perfectionism may impact symptoms of anxiety during the pandemic. We selected anxiety, which refers to an affective state in which an individual feels threatened by the potential occurrence of a future negative event (Dozois, 2006), as an indicator of worsened mental health based on previous findings indicating that individuals high in perfectionism typically do not respond well to uncertainty and/or uncontrollable situations (Flett et al., 1995; Malinger, 2009). Thus, it appeared that levels of anxiety, which are impacted by the unpredictability of potential future events, might have been especially elevated within this population. In this study, we had three specific aims: 1. To examine whether the COVID-19 pandemic has led to increased anxiety in young adults. 2. To determine whether perfectionism plays a role in the experience of anxiety during the COVID-19 pandemic. 3. To determine if the potential relationship between perfectionism and anxiety during the COVID-19 pandemic is mediated by need satisfaction and/or frustration of the need for competence. For these specific aims, we proposed three hypotheses: 1. The effects of the COVID-19 pandemic on young adults would include increased levels of anxiety. 2. Both self-critical perfectionism and personal standards perfectionism would be associated with increasing levels of anxiety; however, this relationship would be stronger for self-critical perfectionism.
3. The potential relationship between perfectionism and anxiety would be mediated by levels of need frustration of the need for competence. Method
Participants The sample for the present study was drawn from two previous, three-wave longitudinal studies; the first study was conducted in the 2018-2019 academic year, and the second study was conducted in the 2019-2020 academic year. The 2018-2019 sample consisted of 379 undergraduate students (84% female; 51% White; 32% Asian; 11% Other; 6% Middle Eastern; 5% Hispanic; 0.8% First Nations) whose ages ranged from 16 to 43 years (M = 20.44, SD = 3.77). The 2019-2020 sample consisted of 295 undergraduate students (83% female; 47% White; 39% Asian; 9% Other; 6% Middle Eastern; 5% Hispanic; 0.4% First Nations) whose ages ranged from 17-53 years (M = 20.70, SD = 3.80). All students were recruited from a large public university in North America.
Procedure For each study, participants were recruited to participate in a year-long study on personal goals. Primary methods of recruitment included flyers and class announcements. Flyers were positioned conspicuously in various locations regularly visited by undergraduate and graduate students (ex. libraries and large lecture halls), and class announcements were made by professors upon request to several undergraduate and graduate classes. At the start of the academic year (T1), participants completed an online survey in which they were asked to identify three personal goals that they planned on pursuing throughout the academic year. Furthermore, the students were asked to indicate their motivation for each goal, as well as to complete measures of perfectionism, anxiety, and need frustration of the need for competence (need frustration). Over the academic year, participants were required to complete follow-up surveys assessing goal progress, goal motivation, need frustration, and anxious symptoms. Although the present study was conducted within the context of a study on goal progress, we did not focus on goal motivation and goal progress as key variables for the present investigation. Rather, our key variables
were perfectionism, anxiety, and need frustration. Consequently, we focused on the assessments completed at baseline (T1), 3 months (T2), and 7 months (T3), as these were the follow-ups in which our key variables were measured. The key measures assessed at each time point were as follows: baseline, perfectionism, anxiety, and need frustration; 3 months, need frustration; 7 months, anxiety.
Measures Perfectionism Perfectionism was assessed at baseline. To assess self-critical perfectionism, 11 items were taken from the Depressive Experiences Questionnaire (DEQ; Blatt et al., 1976). Sample items from this questionnaire included “I tend to be very self-critical” and “There is a significant gap between who I am today and who I would like to be.” The 11 item scale correlates above .85 with the full DEQ self-criticism scale. To measure personal standards perfectionism, we used all 15 items from the Self-Oriented Perfectionism dimension of the Multidimensional Perfectionism Scale (MPS; Hewitt & Flett, 1991). Sample items from this scale included “I strive to be the best at everything I do” and “One of my goals is to be perfect in everything I do”. The Cronbach’s α was .78 for the personal standards perfectionism measure. The items for both self-critical and personal standards perfectionism were dispersed and rated on a 7-point scale, and participants rated from 1 (strongly disagree) to 7 (strongly agree) the extent to which they agreed with each item. Anxiety At all time points, anxious symptoms were assessed using the Generalized Anxiety Disorder Assessment (GAD-7). The GAD-7 was chosen based on previous findings indicating the questionnaire to have strong reliability (Cronbach’s α = 0.89) (Zhong et al., 2015), good test-retest reliability (intraclass correlation = 0.83) and excellent internal consistency (Cronbach’s α = .92) (Spitzer et al., 2006), and to perform well as a measure of anxiety symptom severity (Beard & Björgvinsson, 2014). The scale included items such as “I am not able to stop or control worrying,” and the items were measured on a 4-point Likert scale ranging from
not at all to nearly every day. Need Frustration Need frustration of the need for competence was assessed at baseline and at T2. To assess need frustration, we used the Basic Psychological Need Satisfaction and Frustration Scales (BPNSFS). Previous research indicates this scale to have adequate internal consistency, with Cronbach’s alphas ranging from .73 to .89 for the need satisfaction sub-scales, and from .64 to .86 for the need frustration sub-scales (Chen et al., 2015). To assess need frustration of the need for competence, the scale included items such as “I experienced some kind of failure, or was unable to do something”, and “I did something stupid that made me feel incompetent”. The items were rated on a 5-point Likert scale, ranging from 1 (Completely Disagree) to 5 (Completely Agree).
cantly positively related to anxiety at T3 (r = .47, p < .01). Table 3 shows the partial correlations of self-critical perfectionism and personal standards perfectionism with anxiety and need frustration, controlling for the other perfectionism variable in the 2018-2019 (pre-pandemic) year. Self-critical perfectionism was significantly positively related to anxiety at baseline (r = .44, p < .001), significantly positively related to anxiety at T3 (r = .19, p = .001), and significantly positively related to need frustration at baseline (r = .20, p = .001). Personal standards perfectionism was not significantly related to any of the study variables. Table 4 demonstrates the same partial correlations during the 2019-2020 (pandemic) year. Neither self-critical perfectionism nor personal standards perfectionism were significantly related to any of the other study variables.
Linear Regression Model Results
The analyses that are presented are those based on the 674 participants who completed all relevant measures across all time points. Table 1 presents the means, standard deviations, and correlations for all the study variables in the 2018-2019 (pre-pandemic) year. Personal standards perfectionism scores were significantly higher than self-critical perfectionism scores, t(378) = 3.38, p < .001, d = 0.18. Paired-samples t-test revealed that there was no significant difference in the anxiety scores from baseline to the end of the year, t(302) = -0.89, p = .376, d = 0.06. Anxiety at baseline was significantly positively related to anxiety at T3 (r = .38, p < .01) and was significantly positively related to need frustration at baseline (r = .21, p < .01). Need frustration at T2 was significantly positively related to anxiety at T3 (r = .31, p < .01). Table 2 presents the means, standard deviations, and correlations for all the study variables in the 2019-2020 (pandemic) year. Personal standards perfectionism scores were not significantly higher than self-critical perfectionism scores, t(282) = 0.64, p = .526, d = 0.03. There was not a significant difference between the anxiety scores at baseline and at T3, t(246) = -0.40, p = .689. However, anxiety at baseline was significantly positively related to anxiety at T3 (r = .43, p < .01) and was significantly positively related to need frustration at baseline (r = .53, p < .01). Need frustration at T2 was signifi-
A linear regression was used to examine whether levels of anxiety at the end of the academic year significantly differed between the 2018-2019 and 2019-2020 year. A two-step hierarchical multiple regression was conducted with end of year anxiety as the dependent variable. Baseline anxiety was entered at step one of the regressions to control for anxiety symptoms at the beginning of the year. Data collection year was entered at stage two. Preliminary analyses indicated that gender was unrelated to outcomes and did not moderate the obtained relations, so we did not include this variable. The hierarchical multiple regression revealed that at stage one, baseline anxiety contributed significantly to the regression model, F(1, 548) = 112.76, p < .001, and accounted for 17.1% of the variation in end of year anxiety. Introducing the data collection year variable explained an additional .2% of variation in end of year anxiety and this change in R2 was significant, F(2, 547) = 57.32, p < .001. Data collection year was not a unique predictor, b = .40, t(549) = 1.31, p = .189.
Hierarchical Regression To assess whether either dimension of perfectionism predicted levels of anxiety during the COVID-19 pandemic, we conducted a hierarchical regression. A three-step hierarchical multiple regression was conducted with end of year anxiety as the dependent variable. Anxiety at the beginning of the year was entered at step one of
the regressions to control for existing symptoms of anxiety. The year of data collection, self-critical perfectionism (SC) and personal standards perfectionism (PS) were entered at step two. The two interaction coefficients (SCxYR) and PSxYR) were entered together as a final set of predictors. Preliminary analyses indicated that gender was unrelated to outcomes and did not moderate the obtained relations, so we did not include this variable. The results indicated that the predictor variables accounted for a highly significant multiple R of .434, F(6, 543) = 21.05, p < .001. Each set of predictors were associated with a significant change in R2. Only self-critical perfectionism was a unique predictor, b = .08, t(549) = 2.06, p = .040. Personal standards perfectionism, b = .03, t(549) = 0.82, p = .410, was not a significant predictor. Both interaction variables were not significantly related to anxiety at the end of the year: self-critical perfectionism by year, b = .05, t(549) = 1.40, p = .160; and personal standards perfectionism by year, b = -.03, t(549) = -0.83, p = .410. To determine whether need frustration in March predicted increased anxiety in May, we conducted a hierarchical regression. A four-step hierarchical multiple regression was conducted with end of year anxiety as the dependent variable. Anxiety at the beginning of the year was entered at step one of the regressions to control for existing symptoms of anxiety. The year of data collection was entered at step two. Need frustration at baseline was entered as the subsequent predictor. Need frustration in March was entered as a final predictor. The results indicated that the predictor variables accounted for a highly significant multiple R of .533, F(4, 525) = 52.07, p < .001. Each set of predictors were associ2 ated with a significant change in R . March need frustration was a unique predictor, b = .28, t(529) = 9.02, p < .001 of symptoms of anxiety at the end of the academic year. To explore whether self-critical perfectionism predicted need frustration in March, we conducted a subsequent hierarchical regression. A three-step hierarchical regression was conducted with need frustration as assessed in March as the dependent variable. The year of data collection was entered at the first step. Self-critical perfectionism, personal standards perfectionism and need frustration in September were entered in the second step. The two interaction coefficients (SCxYR) and PSxYR)
were entered together as a final set of predictors. The results indicated that the predictor variables accounted for a significant multiple R of .434, F(6, 528) = 20.37, p < .001. Each set of predictors was 2 associated with a significant change in R . Data collection year was a unique predictor, b = -.10, t(534) = -2.32, p = .021 and the interaction coefficient YRxSC was a unique predictor, b = .14, t(534) = 2.78, p =.006. This revealed that need frustration was lower in the 2019-2020 academic year and that there was an interaction effect in which self-critical perfectionism was associated with greater levels of need frustration in the 2019-2020 year. Self-critical perfectionism did not predict increased levels of need frustration in March, b = 0.02, t(534) = 0.36, p = .720. We had planned to assess whether need frustration mediated the relationship between perfectionism and anxiety using a process mediation model. However, given the initial hierarchical regression results demonstrating that self-critical perfectionism was not associated with need frustration, we were unable to run a process mediation model. Discussion The primary purpose of the present study was to use self-determination theory to assess how levels of self-critical and personal standards perfectionism may influence levels of anxiety during the COVID-19 pandemic. This study used a large sample of participants and included three distinct time points to assess whether levels of need frustration of the need for competence changed over the academic year, and whether these changes influenced anxiety levels. A secondary purpose of this study was to explore whether there were increased levels of anxiety across the pandemic year compared to the pre-pandemic year. Overall, the results did not support our main hypotheses. Primarily, levels of anxiety at the end of the academic year did not significantly differ between the pre-pandemic (2018-2019) year and the pandemic (2019-2020) year. This finding not only contradicted what our hypothesis predicted but also contradicted previous research suggesting that the COVID-19 pandemic is significantly related to increased levels of anxiety (McElroy et al., 2020; Sher, 2020). Self-critical perfectionism significantly predicted levels of anxiety during the pandemic year; however, this was not found for
personal standards perfectionism. The finding that self-critical perfectionism was associated with increased levels of anxiety is consistent with previous work associating self-critical perfectionism with maladaptive outcomes, including anxiety (Dunkley et al., 2020). In contrast, the finding that personal standards perfectionism was not associated with increased levels of anxiety contradicts previous work in this area that suggests that personal standards perfectionism is associated with some negative outcomes, including anxiety (Dunkley et al., 2003; Hill et al., 2004). However, the finding that self-critical perfectionism was associated with increased levels of anxiety, but personal standards perfectionism was not, seems to parallel previous research indicating self-critical perfectionism to have greater deleterious effects on wellbeing when compared to personal standards perfectionism (Stoeber & Otto, 2006). Finally, although need frustration of the need for competence predicted increased levels of anxiety, levels of perfectionism did not predict need frustration. Consequently, need frustration did not mediate the relationship between perfectionism and anxiety. This finding appears to offer some support for previous research suggesting need frustration to be associated with decreased wellbeing (Campbell et al., 2018; Deci & Ryan, 2000; Rodríguez-Meirinhos et al., 2020; Sheldon et al., 2010; Wei et al., 2005). Altogether, the results suggest that while the COVID-19 pandemic may not be associated with increased levels of anxiety, individuals high in self-critical perfectionism appear to experience more anxiety than their peers during the COVID-19 pandemic. The present study not only contributed to the already expansive SDT literature assessing the impact of need frustration on wellbeing but also to the rapidly growing literature assessing risk factors for worsened mental health during the COVID-19 pandemic. We demonstrated that high levels of perfectionism did not seem to be associated with increased levels of anxiety during the COVID-19 pandemic. To further specify, although we found that high levels of self-critical perfectionism were related to increased levels of anxiety, this finding applied to both the pre-pandemic year and the pandemic year, suggesting that the effect of self-critical perfectionism on anxiety was not specific to the context of the COVID-19 pandemic. This contrasts with what we originally predicted. We
hypothesized that increased levels of both self-critical and personal standards perfectionism would be associated with increased levels of anxiety during the COVID-19 pandemic, but that this relationship would be stronger for self-critical perfectionism. However, given that no relationship was identified between personal standards perfectionism and anxiety, the findings were somewhat in line with our predictions that self-critical perfectionism would have more of a detrimental effect than personal standards perfectionism on levels of anxiety. The present study had two key strengths. Primarily, the multi-wave prospective longitudinal design of the study allowed us to assess changes in anxiety and need frustration as a function of levels of perfectionism. Few studies have done this in the context of the COVID-19 pandemic. Additionally, the large number of participants in each sample enabled us to assess the representativeness of the sample and facilitates the generalizability of these results to the broader population of undergraduate university students. Despite these strengths, there are some limitations to consider. Primarily, our samples were limited to undergraduate university students, and within these samples, the majority of participants identified as female. Furthermore, both samples consisted predominantly of White and Asian participants. Future research with more diverse samples is needed in order to extrapolate these findings to different populations aside from undergraduate students. Another limitation is that the time points at which our different variables were assessed during the pandemic year may have only captured early levels of need frustration and anxiety during the pandemic. To further elaborate, baseline levels of perfectionism, need frustration, and anxiety were assessed in the fall 2019 semester, before the COVID-19 pandemic had been declared, and follow-up levels of need frustration and anxiety were assessed in March and May of 2020 respectively. Although the effects of the COVID-19 pandemic were well underway by May 2020, given that the pandemic was only declared in March 2020, these time points did not allow us to assess the trajectory of anxiety and need frustration throughout an entire academic year of enduring the pandemic, as would have been done
if this study was conducted in the 2020-2021 academic year. Furthermore, by May 2020, the academic year for most undergraduate students had already ended. This is especially relevant to consider when assessing university students, whose distress during the COVID-19 pandemic is thought to be in part impacted by a change in teaching methods and academic structure, such as an increased workload, poorer quality of education, and concerns about completing the academic year successfully (Tasso et al., 2021). Future research may want to consider replicating this study in an academic year that consists predominantly of online school. However, given the unpredictable nature of the COVID-19 pandemic, there is no guarantee that such a study will be possible, as it is difficult to predict when the return to in-person classes will occur. Future Directions There are various ways in which future research could expand on the present study. A primary area of interest might be to assess whether levels of self-critical perfectionism and personal standards of perfectionism predict levels of goal motivation and goal progress during the COVID-19 pandemic, and whether these relationships vary as a function of the type of perfectionism. This might be of particular interest given previous research indicating that individuals high in perfectionism have an elevated focus on goal striving (Nepon et al., 2016). Furthermore, previous research also indicates that there is a positive impact of personal standards perfectionism, and a negative impact of self-critical perfectionism, on goal progress (Moore et al., 2018; Powers et al., 2011). Thus, it might be of interest to assess if individuals high in self-critical perfectionism and personal standards perfectionism have a worsened or greater capacity, respectively, to overcome the obstacles and constraints imposed by the COVID-19 pandemic (i.e stay-at-home orders and curfew) to still strive for and achieve one’s personal goals. Looking specifically at the population of university students, future research could also assess the effects of the winter break on students’ levels of anxiety, or general well-being, during the COVID-19 pandemic, potentially looking at levels of perfectionism as a moderating variable. As aforementioned, previous research indicates that having one’s needs for autonomy, competence, and
relatedness satisfied is associated with increased levels of wellbeing (Campbell et al., 2018; Deci & Ryan, 2000; Sheldon et al., 2010; Wei et al., 2005). It is quite typical during the winter holidays for university students to return home to visit their families, and such conditions of being surrounded by loved ones may provide relief from the isolating circumstances that have arisen as a result of the COVID-19 pandemic restrictions. This may result in having one’s need for relatedness satisfied, which may in turn decrease levels of anxiety and increase well-being. These effects could potentially be moderated by levels of self-critical perfectionism and personal standards perfectionism. Self-critical perfectionism is associated with the personality trait of neuroticism, and this tendency towards experiencing negative affect may potentially influence one’s interpersonal style with other individuals, which may have an effect on one’s overall well-being (Costa & McCrae, 1992; Dunkley et al., 2020). Finally, future research could also assess whether being involved in therapy may serve as a protective factor against the effects of the COVID-19 pandemic on one’s anxiety levels, once again assessing whether this relationship between therapy and levels of anxiety is moderated by levels of self-critical and/or personal standards perfectionism. This might be of interest to assess, as previous work by Blatt et al. (1995) has suggested that self-critical perfectionism predicts negative outcomes in the short-term treatment of depression. Subsequent research indicated that this relationship between self-critical perfectionism and negative treatment outcomes was mediated by the inability of individuals high in self-critical perfectionism to develop strong therapeutic alliances (Zuroff et al., 2000). Furthermore, Richardson & Rice (2015) found that individuals high in self-critical perfectionism were less likely to engage in disclosure when under high levels of stress, a time when disclosure is most beneficial, compared to those low in self-critical perfectionism. Thus, during a time in which the wellbeing of many individuals seems to have been severely impacted, it may be beneficial to assess the various mechanisms which may enhance or diminish the effects of therapy on one’s wellbeing. In conclusion, the COVID-19 pandemic poses a significant challenge to individuals around the
world, and it is critical to continue assessing the potential risk factors for worsened wellbeing during these times. The present investigation makes an important contribution to the rapidly expanding COVID-19 literature, as well as the already expansive SDT literature, by demonstrating the value of utilizing the self-determination theory as a theoretical framework to understand what role levels of perfectionism may play in influencing levels of anxiety. The results of our study did not show that levels of anxiety were disproportionately influenced by levels of perfectionism during the COVID-19 pandemic. However, in line with previous research, self-critical perfectionism was found to predict increased levels of anxiety, and it is important to continue assessing the mediating mechanisms that correlate trait perfectionism with worsened wellbeing.
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Perfectionism, Self-Care, Motivation, and Depressive Symptoms in University Students During the Pandemic Ignacio Perez Montemayor Cruz1 Richard Koestner2. Shelby Levine3
Department of Psychology, McGill University PhD Candidate, Department of Psychology, McGill University 3 Professor, Department of Psychology, McGill University 1
Abstract Background The COVID-19 pandemic has been responsible for increasing stress levels and mental health problems in the general population and potentially more so in already vulnerable populations such as individuals higher in perfectionism. This study examined whether university students higher in perfectionism experienced more depressive symptoms during the pandemic and whether their engagement in self-care and their motivation for self-care could partially explain why. Methods At time 1, 345 students completed a survey that measured depressive symptoms, perfectionism, self-care, and motivation. Four months later, 169 students were reassessed in a follow-up survey for depressive symptoms, self-care, and motivation. Results Students higher in self-critical perfectionism were more likely to experience depressive symptoms, less likely to engage in self-care activities, and less likely to be autonomously motivated to engage in self-care. Conclusion Contrary to hypothesis, the lack of engagement in self-care practices did not mediate the strength of the association between their perfectionism and their mental health. The relationship between self-care and depression indicates that further research should be conducted regarding self-care and its impact on mental health. Keywords: Perfectionism, Depressive Symptoms, Motivation, Self-Determination Theory, Self-care.
Introduction The COVID 19 global pandemic and the lockdown measures taken to prevent its spread have caused a significant amount of psychological distress in the Canadian population (Best et al. 2020). A meta-analysis of 21 studies has found that the pandemic has significantly affected the mental health of children, adolescents, and young adults (Stavridou et al., 2020). People scoring higher on the trait of perfectionism could be at risk of experiencing a worsened state of mental health during the pandemic. Indeed, these individuals were already more likely to be experiencing higher levels of stress and negative affect, and perfectionism has been previously associated with higher reactivity to stressful events (Dunkley et al., 2003; Dunkley et al., 2014; Dunkley, 2018; Mandel et al., 2015). The overall goal of our study was to take this once-in-a-century opportunity to further probe the mechanisms that mediate the relationship between perfectionism and psychological distress. One potential mediator, we proposed, is the amount of engagement in self-care. The concept of self-care has exploded in popularity in recent years; in theory, greater self-care should be associated with better mental health, but research is limited to date. Thus, the present study will examine whether students higher in perfectionism are less likely to engage in self-care and whether their lack of engagement partially explains their mental health issues during the pandemic. In addition, adopting a self-determination theory perspective, we will also explore whether the type of motivation for self-care plays a role in the association between perfectionism, self-care, and mental health.
Perfectionism Research on the personality trait of perfectionism has established that it can be divided into two dimensions called personal standards (PS) Perfectionism and self-critical (SC) Perfectionism (Dunkley et al., 2000; Dunkley et al., 2003). Individuals scoring higher on PS Perfectionism tend to set high goals, or standards, for themselves and work hard to accomplish them (Dunkley et al., 2000; Dunkley et al., 2006). On the other hand, people scoring high on SC Perfectionism tend to set high goals, or standards, for themselves but tend to severely berate themselves for falling short of said standards (Dunkley et al., 2003). People with high levels of SC Perfectionism are also extremely sensitive to how they are evaluated by others (Dunkley et al., 2003). In the five-factor model of personality, PS Perfectionism has been mainly associated with conscientiousness, whereas SC Perfectionism has been mainly associated with neuroticism (Dunkley et al., 2006).
Both dimensions of perfectionism have a broad impact on people’s lives as they have each been associated with particular attachment styles, motivational tendencies, and coping styles (Dunkley et al., 2012; Dunkley et al. 2000; Dunkley et al. 2003; Moore et al., 2020; Stoeber et al., 2018). Of note, over the past few decades, perfectionism levels have been significantly increasing in the general population and even more so in young student populations, who report particularly high levels of SC Perfectionism (Curran & Hill, 2019; Sironic & Reeve, 2015; Smith et al. 2019; Stornaes et al., 2019). Flett and Hewitt have expressed worry concerning this recent trend, which they have coined as the “Perfectionism pandemic” (Flett & Hewitt, 2020). They argue that increasing perfectionism could contribute to the current global mental health crisis, given the negative outcomes associated with it (Flett & Hewitt, 2020). Such concern would be particularly warranted in the case of the aforementioned young student populations, as SC Perfectionism has been associated with psychological distress and with a vulnerability to mental health problems (Dunkley et al., 2003; Mandel et al., 2015; Sironic & Reeve, 2015).
Perfectionism and Mental Health SC Perfectionism has been consistently associated with negative outcomes in the domain of mental health (Stoeber et al., 2018). Consistent with this, SC Perfectionism is associated with higher levels of negative affect and lower levels of positive affect (Dunkley et al., 2003; Milyavskaya et al., 2014; Richard et al., 2020). Furthermore, multiple studies have established a link between SC Perfectionism and life stress (Bieling et al., 2004; Dunkley et al., 2014; Sironic & Reeve, 2015). As a result of their strong desires for achievement, productivity, and approval, people scoring high on SC Perfectionism find achievement-focused environments and situations where one faces a significant loss of control to be especially stressful (Dunkley, 2017; Dunkley et al., 2020). This pattern of heightened stress is particularly harmful given that people high in SC Perfectionism tend to respond to stress with feelings of depression (Mandel et al. 2015). In addition to these situations, SC Perfectionists have also been found to respond to negative social interactions with heightened increases in sadness, a response that can undermine social support and cause more stress in the long term (Mandel et al., 2018). Thus, SC Perfectionists are likely to get stuck into a vicious circle: they experience higher stress levels than others and respond poorly to said stress, resulting in even more stress for them down the line. Most importantly, SC Perfectionism is also strongly associated with a vulnerability for psychological disorders. Indeed, numerous studies have found that individuals higher in SC Perfectionism experience increased general distress, depressive symptoms, and anxious symptoms over time (Dunkley et al., 2020; Levine et al., 2019; Mandel et al., 2015; Mandel et al., 2018; Sironic & Reeve, 2015; Tobin & Dunkley, 2021). Furthermore, due to its continued impact on stress and negative affect, SC Perfectionism has also been found to contribute to the maintenance of psychological disorders like depression (Egan et al., 2011; Levine et al., 2020; Richard et al., 2020). Overall, there is a strong consensus regarding the label of SC Perfectionism as a maladaptive trait (Egan et al., 2011). For these reasons, SC perfectionists may be particularly vulnerable in times of widespread stress, such as in the current pandemic (Flett & Hewitt, 2020). Findings have not been as clear in the case of PS Perfectionism. Some research has shown that PS Perfectionism could be associated with mild
negative outcomes. Indeed, some studies have shown that PS Perfectionists share SC Perfectionism’s vulnerability to achievement-related stress (Milyavskaya et al., 2014; Richard et al., 2020). Nevertheless, evidence suggests that SC Perfectionism is more strongly associated with negative mental health outcomes than is PS Perfectionism (Dunkley et al., 2006; Dunkley et al., 2014). A meta-analysis suggests that PS Perfectionism is associated with positive characteristics and negatively associated with the negative characteristics of SC Perfectionism (Stoeber & Otto, 2006). The third set of findings suggests that PS Perfectionism is more of a “neutral” perfectionism, unrelated to mental health (Bieling et al., 2004; Levine et al., 2019). Taken together, these findings suggest caution about classifying PS Perfectionism as “functional” or “adaptive,” as one might be eager to do, especially when comparing it to SC Perfectionism (Stoeber & Otto, 2006).
Self-care The concept of self-care has become increasingly popular in recent years. Indeed, Google searches for self-care have been consistently on the rise, reaching an all-time high at the peak of the COVID pandemic (Schieber, 2020). The interest in self-care has also manifested itself in the market as self-care-related books and apps fly off both virtual and actual shelves (Ali, 2019; Schieber, 2020). From a psychological standpoint, self-care can be thought of as a type of non-specific coping; it is not problem-focused but rather aims at improving well-being and, arguably, is fueled by self-compassion. While there is currently no formal research definition of self-care, we operationalized self-care as healthy habits and relaxation activities used to handle the stresses and uncertainties caused by global pandemic. As previously stated, there is little research surrounding self-care and none regarding its link to perfectionism, but there is some research linking each dimension of perfectionism to a particular coping style. Indeed, PS Perfectionism is associated with a tendency to engage in active coping (Dunkley et al., 2000; Dunkley et al., 2017). On the other hand, SC Perfectionism is associated with a tendency to engage in avoidant coping (Dunkley et al., 2003; Dunkley et al., 2017; Van der Kaap-Deeder et al., 2016). This maladaptive coping
tendency has been found to produce more stress and negative affect over time and partially mediate the relationship between SC Perfectionism and distress (Dunkley et al., 2003; Dunkley et al., 2014; Dunkley et al., 2017). Thus, perfectionism has been found to differentially predict coping styles and may factor in how individuals cope during the pandemic.
Motivation Self-Determination Theory examines motivation quality and the influence that different types of motivation have on our behavior, goal progress, well-being, and life satisfaction (Ryan & Deci, 2017). The theory mainly divides motivation into two types: autonomous (or intrinsic) and controlled (or extrinsic) motivation (Ryan & Deci, 2017). Autonomous motivation refers to when one’s motivation is inherently self-chosen because it fuels actions that reflect our values and who we truly are (Ryan & Deci, 2017). Controlled motivation refers to when one’s motivation is a result of external factors, be they positive (e.g., the incentive of rewards) or negative (e.g., the pressure of fulfilling your parents’ expectations) (Ryan & Deci, 2017). Consequently, individuals are more likely to repeatedly engage in behaviours that are autonomously motivated compared to those that are motivated by control (Ryan & Deci, 2017). As previously mentioned, the perfectionism dimensions have been associated with these different motivational approaches: SC Perfectionism has been previously associated with a tendency for high controlled motivation and low autonomous motivation, whereas PS Perfectionism has been associated with high autonomous motivation (Moore et al. 2020; Stoeber et al. 2018). Nevertheless, PS Perfectionism has also been positively linked with controlled motivation, although not as strongly as SC Perfectionism, further highlighting the mixed nature of PS Perfectionism (Mouratidis & Michou, 2011; Steober et al. 2018).
The present study Perfectionism and self-care appear to be increasingly important factors in the domain of mental health, but further research is needed to fully understand their effects. The aim of the present research was to determine whether self-care and the motivation for self-care mediate the relationship between perfectionism and depressive symptoms in the context of the pandemic. We hypothesized that: (1) individuals higher in self-crit-
ical perfectionism would experience significantly more symptoms of depression overtime during the pandemic; (2) individuals higher in self-critical perfectionism would report engaging in less self-care; (3) to the extent that individuals high in SC perfectionism engaged in self-care, their motivation for doing so would be more controlled than autonomous; (4) motivation for self-care and self-care would partially mediate the relationship between self-critical perfectionism and symptoms of depression. Considering the mixed research concerning PS Perfectionism, we adopted a more exploratory approach and did not formulate any a priori hypotheses regarding a mediation model with PS Perfectionism at its center. To test our hypotheses, we conducted a longitudinal survey that assessed SC and PS perfectionism, depressive symptoms, self-care, and motivation for self-care over the course of an academic term in a large Canadian University.
Participants We recruited 345 students from a large Canadian university to participate in our online study in September 2020. The sample was composed of generation z (aged 25 or less) undergraduate university students (Mage = 19.75, SD = 1.41) and was mainly female (87.9%). The sample was 57.6% White, 30.6% Asian/Asian Canadian/ Pacific Islander, 8.2% Middle Eastern, 2.3% Latino/ Hispanic American, and 1.3% Black/African American. The participants were recruited through the university subject participant pool in September (Time 1), and willing participants completed a follow-up survey in December (Time 2). One hundred and sixty-nine participants completed the December follow-up, making our retention rate 48.98%. The participants were compensated with school credit in September and with the chance to win a 100$ raffle in December. The study was approved by the McGill University Research Ethics Board and conducted in accordance with the Declarations of Helsinki.
Procedure At time 1 (T1), consenting participants were invited to complete our survey, which was administered on Qualtrics. The surveys assessed SC Perfectionism,
PS Perfectionism, depressive symptoms, self-care engagement, autonomous motivation for self-care, and controlled motivation for self-care, and took approximately 30-mins to complete. At time 2 (T2), participants were recontacted by email and invited to participate in the follow-up study. Again, the survey was administered on Qualtrics, and the same questionnaires were administered.
Measures Perfectionism We assessed perfectionism in our survey using two subscales of the Multidimensional Perfectionism Scale (MPS) (Hewitt & Flett, 1991). We used all items in the socially-prescribed perfectionism dimension of the MPS to assess SC Perfectionism. This subscale is composed of items such as “Anything that I do that is less than excellent will be seen as poor work by those around me” and “The people around me expect me to succeed at everything I do” (Hewitt & Flett, 1991). We used all items in the self-oriented perfectionism dimension of the MPS to assess PS Perfectionism. This subscale is composed of items such as “I must always be successful at school or work” and “I must work to my full potential at all times” (Hewitt & Flett, 1991). In our survey, the items from both scales were mixed, and participants had to rate each of them on a Likert point-scale of seven, going from “strongly disagree” to “strongly agree,” making higher scores indicate higher levels of perfectionism. The ratings of these items would then be averaged, resulting in a mean PS Perfectionism and a mean SC Perfectionism score. Depressive Symptoms To assess depressive symptoms in our participants, we included the ten items of the Centre for Epidemiologic Studies Depression Scale-Revised (CESD-R 10) in our survey (Andresen et al., 1994). The CESD-R 10 is a self-report measure focused on the affectivity component of depressed mood and is thus composed of items such as “I felt everything I did was an effort” and “I felt lonely” (Andresen et al., 1994). The participants rate the ten items on a Likert point-scale of four, going from 1 “rarely or none of the time (<1 day)” to 4 “most or all the time (5 - 7 days).” The scores of the ten items are averaged to obtain a depressive symptoms score for the participant, where a higher score indicates experiencing more depressive symptoms. The scale has been previously validated and found to be reliable (Andresen et al., 1994).
Self-care We formulated our own scale to measure self-care in our participants. In the context of our survey, we conceptualized self-care as behaviors done to deal with the uncertainty caused by COVID-19. Thus, we asked participants to rate their degree of engagement in the following six activities: “reaching out to others,” “following a balanced/healthy diet,” “taking the opportunity to pursue hobbies,” “following a routine,” “engaging in physical exercises,” “engaging in relaxing activities (e.g., listening to music, gardening, etc.).” They would rate their engagement in a Likert point-scale of seven, ranging from 1 “strongly disagree” to 7 “strongly agree.” We verified the internal validity of the scale to ensure it would be usable for this study. Motivation for Self-care For each of the self-care items, the participant had to answer five items regarding their motivation to engage in each specific behavior. These five items would list different reasons for engaging in the behavior, and the participant would rate how much his behaviour was influenced by each of the five motivations on a Likert point scale of seven, ranging from 1 “not at all for this reason” to 7 “completely for this reason.” Two of the items assessed controlled motivation, as they proposed reasons such as “Because somebody else wants you to, or because you will get something from somebody if you do” (Sheldon & Elliot, 1999). The other three items assessed autonomous motivations, as they proposed reasons such as “Because it represents who you are and reflects what you value most in life” (Sheldon & Elliot, 1999). The mean scores for autonomous and controlled motivation would be calculated from the answers across self-care items. Results
Preliminary Analysis Table 1 presents the means, standard deviations, and the correlation between all the variables of interest. Depressive symptoms were relatively stable through the semester as depressive symptoms at baseline were correlated with depressive symptoms at T2. While the two dimensions of perfectionism, measured at baseline, were correlated to depressive symptoms at both time points, SC perfectionism was more strongly correlated in both cases. Self-care at T2 and autonomous motivation for self-care at T2 were positively correlated. Furthermore, both were negatively
correlated with depression at both times. On the other hand, controlled motivation for self-care at T2 was uncorrelated to self-care at T2. SC Perfectionism was negatively correlated to both T2 self-care and autonomous motivation for self-care and positively correlated to controlled motivation for self-care at T2. PS Perfectionism was positively correlated to controlled motivation for self-care at T2 but showed no significant correlation to self-care and autonomous motivation for self-care.
Regression Analyses Depressive symptoms Controlling for depressive symptoms at baseline, Perfectionism was associated with depressive symptoms in December, accounting for 39.7% of the variance (R²=.4, F(3, 158) = 34.7, p < 0.001). SC Perfectionism at T1 predicted more depressive symptoms at T2 (b = 0.11, t = 2.51, p < .05). PS Perfectionism did not predict more depressive symptoms at T2 (b = 0.01, t = 0.1, p = .92). Self-care Perfectionism was marginally associated with self-care in December, accounting for 3.6% of the variance (R²=.04, F(2, 159) = 2.93, p = .56). SC Perfectionism at T1 predicted less self-care at T2 (b = -.19, t = -2.07, p < .05). PS Perfectionism was not associated with self-care at T2 (b = -.03, t = -.26, p = .8). Autonomous Motivation for Self-care Perfectionism was marginally associated with autonomous motivation for self-care in December, accounting for 3.1% of the variance (R²=.03, F(2, 159) = 2.53, p = .08). SC Perfectionism at T1 predicted less autonomous motivation for self-care at T2 (b = -.2, t = -2.20, p < .05). PS Perfectionism was not associated with autonomous motivation for self-care at T2 (b = .06, t = .52, p = .59). Controlled Motivation for Self-care Perfectionism was associated with controlled motivation for self-care in December, accounting for 6.8% of the variance (R²=.07, F(2, 159) = 5.82, p < .01). SC Perfectionism at T1 predicted more controlled motivation for self-care at T2 (b = .25, t = -2.35, p < .05). PS Perfectionism was not associated with controlled motivation for self-care at T2 (b = .17, t = 1.24, p = .22).
Mediation Analyses We attempted to further understand how higher levels of SC Perfectionism could lead to experiencing higher levels of depressive symptoms. As new mediators, we proposed self-care engagement and either autonomous or controlled motivation for self-care. We ran our mediation model on SPSS. For both mediation analyses, we estimated 95% confidence intervals for the indirect effect using bootstrap resampling (k = 5000) procedures. Figures 1 and 2 show that, while there was a direct effect of SC Perfectionism on depressive symptoms, neither mediation model was supported. Neither autonomous motivation for self-care and self-care nor controlled motivation for self-care and self-care provided a significant indirect explanation for the link between SC Perfectionism and depressive symptoms.
Discussion While our results were not entirely what we expected, we were able to confirm some of our predictions. Indeed, consistent with our first hypothesis, scoring higher on SC perfectionism at baseline predicted experiencing more depressive symptoms over the fall term. This result supports the research that has established SC Perfectionism as a risk factor for depression (e.g., Mandel et al., 2015). As we hypothesized, higher SC Perfectionism predicted less self-care at T2. Thus, unless future studies fail to replicate this result, decreased self-care could be added to the list of negative outcomes related to SC Perfectionism. Confirming our third hypothesis, SC Perfectionism also predicted reporting less autonomous motivation and more controlled motivation for engaging in self-care. These findings are consistent with previous research, which has linked SC perfectionism to a tendency for reporting higher controlled motivation and lower autonomous motivation (e.g., Stoeber et al., 2018). Overall, these findings contribute to the literature that establishes the maladaptive nature of SC Perfectionism (e.g., Egan et al., 2011) and thus further justify Flett and Hewitt’s concern over the rising levels of perfectionism (Flett & Hewitt, 2020). Conversely, as shown in our results section, our data found no link between PS Perfectionism and our variables of interest. These null findings would be in line with the body of research that has found PS Perfectionism to be unrelated to mental health (e.g., Bieling et al., 2004).
Our third hypothesis examined two mediation models in which the following mediators were tested: controlled motivation, autonomous motivation, and engagement in self-care. These mediators were tested to determine whether they partially explain the association of self-critical perfectionism with depressive symptoms. Our initial reasoning was that individuals higher in SC Perfectionism would approach self-care as something they have to do (i.e., with a more controlled motivation). Their controlled motivation could perhaps originate from seeing people around them or, most likely, in social media preaching the benefits and importance of self-care, which would make them feel like not making an effort to engage in self-care would be a social (or even personal) failure. Alternatively, individuals higher in SC Perfectionism could also be more prone to prioritize their everyday work (academic or otherwise), to the point of not having any strong desire of taking care of themselves if it means taking time off (i.e., having little to no autonomous motivation). They might even perceive time spent engaging in self-care as wasted time. Both motivational approaches would translate to people scoring high in self-critical perfectionism engaging in less self-care overall, which would then partially explain their higher levels of depressive symptoms. Regardless, as previously stated, neither mediation model was supported by our data. Indeed, our results indicate that the impact of SC Perfectionism on the type of motivation for engaging in self-care and the subsequent decrease in engagement in self-care are not significantly useful in accounting for the link between SC Perfectionism and depressive symptoms. As an additional exploratory analysis, we also conducted a mediation analysis with a simplified model that got rid of the motivational component. Our data did not support this simplified model either. Interestingly, we did find a significant moderate correlation between self-care and depressive symptoms at T2 (r = -.38, p < 0.01). This result could be interpreted as a validation of our interest in self-care in the context of researching mechanisms surrounding the deterioration of mental health. On the other hand, an alternative explanation could be that an increase in depressive symptoms leads to a decrease in self-care. Indeed, decreased self-care could be a result of a “Markedly diminished interest or pleasure in all, or almost all, activities,” which is one of the symptoms of depression outlined by the
DSM-V (American Psychiatric Association, 2013). Thus, higher SC Perfectionism could perhaps be leading to decreased self-care through its impact on depressive symptoms. Alternatively, the link between SC Perfectionism and depressive symptoms could also be unrelated to the association between SC Perfectionism and self-care. The effect of a decrease of self-care in people higher in SC Perfectionism could perhaps lead to a decrease in well-being (instead of an increase of depressive symptoms). Limitations and Future Directions A major limitation of this study was the limited resources surrounding the concept and measurement of self-care. Indeed, despite having confirmed its internal reliability, our scale for self-care remains nonetheless quite experimental. Our scale could be changed in many ways to better reflect the concept of self-care. For example, the list of items could be expanded by adding items such as “making sure not to neglect my personal hygiene routine” and “maintaining a stable sleeping schedule.” Furthermore, our scale measures self-care in holistic terms, but, given the relative lack of research surrounding this topic, it might be possible that that is not the most effective or accurate way of conceptualizing and measuring self-care. Indeed, perhaps having to score high on all six (and potentially more) of our items to obtain a high score in self-care might misrepresent how people actually engage in self-care. Self-care might not be the same for everyone. For example, introverts might just not consider “reaching out to others” as self-care. As an aggregated score would not be adequate anymore, this perspective of self-scare would require a retooling of how the score is obtained from the scale, an endeavour that could be taken up by future research. As established in the previous paragraph, future research could attempt to improve or reformulate the scale we created to measure self-care. This would be particularly important if research on self-care and how it impacts mental health is to continue, which it should, considering its aforementioned link to depression. Indeed, two questions immediately arise from this association: is decreased self-care causally related to increased depression and, if so, is it an antecedent or a consequence of it? The answers to these questions could potentially open many other direc-
tions for research regarding self-care and mental health. As previously suggested, future research could also examine whether an increased engagement in self-care improves well-being. Finally, these research directions could culminate in finding ways to promote an autonomously motivated practice of self-care in schools, universities, workplaces, and clinical settings where it could help those at risk of experiencing increased symptoms of depression. Conclusion The aim of this study was to further understand the association between perfectionism and mental health and to investigate whether motivation for self-care and self-care could be playing a mediator role in this association. To test our hypotheses, we administered a survey that measured our variables of interest at two time points, four months apart. The survey consisted of established scales for all of our variables of interest except for self-care, for which there were none. Thus, we used a self-care scale which we created for this study. While PS Perfectionism was unrelated to our variables of interest, SC Perfectionism predicted higher depressive symptoms, a more controlled than autonomous motivation for engaging in self-care, and less engagement in self-care. Nevertheless, the results did not support our mediation model, showing that it is not through the motivation for self-care and self-care that higher Self-critical Perfectionism leads to more depressive symptoms. Future directions for research include improving the measuring techniques for self-care and further exploring the association between self-care and mental health.
Means, standard deviations and correlations between all variables of interest. M (SD)
SC Perfectionism and PS Perfectionism predicting controlled motivation for self-care at T2 Variable
1. Depression T1
2. Depression T2
3. PS Perfectionism
4. SC Perfectionism
5. Self-care T2
6. Self-care AM T2
7. Self-care CM T2
Self-care CM T2 b
Note. CM = Controlled motivation; T2 = Time 2; SC = Self-critical; PS = Personal Standards. Bolded values are significant p < .05
Figure 1 Direct and Indirect effects of SCP on depressive symptoms via autonomous motivation for self-care and self-care
Note. T1 = Time 1; T2 = Time 2; PS = Personal Standards; SC = Self-critical; AM = Autonomous motivation; CM = Controlled motivation. * p < .05, ** p < .01, *** p < .001.
Table 2 Baseline depression, SC Perfectionism, and PS Perfectionism predicting depression at T2
Depression T2 b
Note. T1 = Time 1; T2 = Time 2; SC = Self-critical; PS = Personal Standards. Bolded values are significant p < .05
SC Perfectionism and PS Perfectionism predicting self-care at T2 Variable
Self-care T2 b
Note. T2 = Time 2; SC = Self-critical; PS = Personal Standards. Bolded values are significant p < .05
Table 4 SC Perfectionism and PS Perfectionism predicting autonomous motivation for self-care at T2 Variable
Self-care AM T2 b
Note. AM = Autonomous motivation; T2 = Time 2; SC = Self-critical; PS = Personal Standards. Bolded values are significant p < .05
Direct and Indirect effects of SC Perfectionism on Depressive Symptoms via controlled motivation for self-care and self-care
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McGill Psychology Undergraduate Research Journal Issue XIII April 2022