Abstract 4: Mental Health Screening Practices for Student-Athletes at Canadian Post-Secondary Institutions
Bibliographic record
Abstract
Student-athletes are not immune to mental health disorders. Many post-secondary institutions conduct regular screenings to monitor student-athletes’ mental health. In the United States, there exists substantial variability in institutional screening practices (e.g., choice of self-reported questionnaire, referral system), which may affect the rate of early detection of student-athletes in need. Evidence on practices of mental health screenings for student-athletes at Canadian post-secondary institutions is presently absent from the literature. The present study sought to fill this gap. Head athletic therapists at all 56 USPORT institutions across Canada were invited to participate in the study. Staff members from twenty-four (43%) institutions across all four Canadian USPORT divisions completed a Qualtrics survey with Likert scale and open-ended questions. The results revealed that 96% of all participating institutions conducted regular screenings of student-athletes’ mental health using self-reported questionnaires. A large variability in the choice of measures, administration type and frequency was detected. Mental health information was most often shared with the athletic therapy staff and team physicians. In some cases, the information was also shared with the coaching staff. Referral procedures of athletes who were flagged for elevated mental health symptoms varied from no follow-up/unclear procedures to being contacted directly by mental health clinical staff. Underreporting and lack of designated staff hours were mentioned as barriers to the screenings’ effectiveness. While many Canadian post-secondary institutions conduct regular mental health screenings, clear guidelines may aid to decrease the variability in screening practices and improve the mental health care for Canadian student-athletes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".