Responding to Mental Health Challenges in an Ontario University Athletic Department: An Institutional Analysis
Bibliographic record
Abstract
It has been well-documented that student-athletes experience additional mental health challenges compared to their non-athlete peers. Universities can play an integral role in mitigating these challenges. Using an Institutional Ethnographic (IE) approach, this study examines an Ontario university athletic department’s institutional practices and responses to student-athlete mental health. Specifically, this study elucidates the institutional organization and structure of the athletic department and university responses to student-athlete mental health challenges. By adopting the perspective of a student-athlete, difficulties that the student-athlete experiences when pursuing help for their mental health challenge are identified. In-depth interviews were conducted with seven institutional workers and one student-athlete along with the analysis of institutional texts present in the process of help-seeking. The data demonstrated the process of a student-athlete navigating a triage model of mental health care - highlighting the influencing ruling and social relations, a highly team dependent and collaborative process, varying levels of responsibility for institutional workers, and a lack of funding and staffing. There was also an emphasis placed on the university being a leader in mental health, often leading institutional workers and student-athletes to have certain expectations regarding the mental health services offered. The thesis concludes with a discussion of strategies to reform the process of student-athletes receiving help for their mental health challenges, which include: 1) establishing clearer responsibilities for workers’ roles and more straightforward paths for student-athletes to navigate, 2) employing a mental health professional for athletes, who is available and accessible for student-athletes to receive timely care, and who could help to eliminate the need for a complicated and inconsistent triage \nsystem, 3) proactive screening for mental health challenges with student-athletes rather than the use of a reactive model, 4) integration of a training readiness survey to help integrated support teams monitor and identify the wellbeing of student-athletes, 5) required training for all coaches and athletics office staff related to mental health first aid, 6) better, more realistic communication about the university’s standing as a leader in mental health, and 7) overall better integration of mental health services into athletics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".