Lilly Bautista, Rachelle Clayson, Jared Klundt and Davey Erekson, Counseling & Psychological Services

Introduction

Within the college population, student athletes are often considered a unique population for mental health concerns. There are more student athletes than ever before, with over 460,000 students participating in the National Collegiate Athletic Association (NCAA, 2016). Student athletes have access to more resources than the typical student, but the amount of utilization and extent of these resources can vary greatly. Specifically, the utilization of university and college counseling centers.

There is a positive correlation between physical and mental health that may provide mental health benefits for student athletes. Downs and Ashton (2011) found that students who maintain a high level of vigorous physical activity (such as student athletes) experience better physical and mental health outcomes than their peers (Downs & Ashton, 2011). As described in this article, the tie between mental and physical health serves an added benefit for student athletes. However, if a student athlete begins experiencing decreased mental health, this tie would implicate a corresponding decrease in their physical performance. Therefore, utilization of university/college counseling centers may have significant health benefits for student athletes.

The purpose of our project is to investigate the mental health risks between student athletes attending college/university counseling services. Do student athletes mental health risks (anxiety, hostility, depression, substance abuse, etc.) differ from non-student athletes attending university sanctioned counseling centers, and if they do, what differs? Since no known systematic research has used the Counseling Center Assessment of Psychological Symptoms Assessment 62 (CCAPS-62) to obtain such results, we analyzed such data in order to shed light to a potential existing relationship between athlete status and utilization of counseling centers, shifting changes in student athletes’ mental health.

Methods

We used an archival database from the Center for Collegiate Mental Health (CCMH) 2013-2015. It is one of the largest database of collegiate mental health measures studied in the past five years. We had access to over 10,000 student athletes CCAPS-62 measure from over 139 universities across the nation. This data-set includes responses from the Counseling Center Assessment of Psychological Symptoms 62 (CCAPS-62). Students participate in this high quality, multi-dimensional assessment instrument as part of their initial visit to their university’s counseling center. We analyzed the data retrieved from the CCAPS-62 using SPSS software version 25; establishing measures on student visits, athletic status, gender, and predicted power change in mental health risks. We calculated the client’s eight subscales scores from the CCAPS- 62 (Anxiety, Depression, Alcohol Use, Hostility, Eating Concerns, Social Anxiety, Family Distress, Academic Distress) using ANOVA and linear regression.

Results

There were males (33.3%) and females (66.6%) in our sample. The average mean age was 22.46 with the median being 20.08. We ran a preliminary analysis and found that being a student athlete significantly predicted hostility (β =0.138, p < 0.05) and alcohol use (β = .191, p < 0.05) tendencies. However, when we controlled for counseling session length(s) and initial severity, we did not find any differences in slope between athletes and non-athletes. Due to limited data, we were unable to determine a correlation between hostility and alcohol use, changes over time throughout counseling visits in student athletes versus non-athletes.

Discussion

Using an existing dataset provided us with a larger and more representative data-set that we otherwise would have been limited to if we would have conducted it within Brigham Young’s Psychological and Counseling Services. Our study had limitations in which we were constrained by time and limited data that prevented us from fully exploring our research question. We hope to continue to work on this project and find alternative ways to analyze the eight subscales overtime comparing student athletes and non-student athletes visiting their university counseling centers. With more time, we hope to detect specific factors that counseling centers offer to student athletes that specifically help mitigate stressors and their likelihood of mental health risks. This would require more information from the data set such as session length and duration of the clients visit(s). In due time, identifying specific variables that contribute to lessening the severity of mental health risks in student athletes, we can increase awareness of the significance that athletes can have on their mental wellbeing from utilizing counseling centers.