Building the Foundation for Sport Mental Health Policy: Addressing Student-Athlete Mental Health at the University of Calgary
Bibliographic record
Abstract
Varsity student-athletes are a unique subgroup of students that require identity-specific mental health resources. However, student-athlete mental health is significantly under-researched and therefore many USPORTS member universities, the UofC included, do not have a mental health policy specifically for student-athletes. This is problematic since student-athletes report higher levels of psychological distress due to the social determinants of mental health (SDMH) in comparison to the general student population and therefore require specialized supports. In this study I sought to explore the mental health resources available to student-athletes in comparison to the student population across USPORTS member universities. The goal of this research was to determine whether the UofC owes its student-athletes a duty of care, and if the Dinos Athletics department should develop a policy for student-athlete support accordingly, to mitigate the effects of the social determinants of mental health (SDMH). I conducted a structured literature review, a jurisdictional scan and a website and document analysis to identify USPORTS member universities and their best practices in providing a duty of care for student-athlete mental health. Participant universities demonstrated various approaches in implementing mental health resources and included a hybrid of upstream and downstream services. The findings show that in comparison to other member universities, the Dinos Athletics department and the UofC are behind in providing adequate athlete-specific mental health resources. Findings from this study can inform the Dinos Athletics department and the UofC Board of Governors in safeguarding the mental health of student-athletes.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".