Sports psychiatry clinical curriculum for sports and exercise medicine fellows in one Canadian university
Bibliographic record
Abstract
Abstract: Introduction: Athletes can experience the same mental illnesses as the general population. Sports create additional circumstances that can influence the mental health of its participants. Sport and Exercise Medicine (SEM) physicians are often on the front lines of providing medical assessment and treatment to athletes for a variety of presenting complaints. Enhancing the knowledge base and clinical approach of Sport and Exercise Medicine physicians for assessment and management of mental health challenges in athletes can assist in early identification, assessment, treatment initiation, or referral to a sports psychiatrist. Providing SEM trainees with formal clinical exposure to Sports Psychiatry can better equip them for their future practice. This article describes a formalized clinical rotation in Sports Psychiatry for SEM Fellows in one Canadian university. Methods: In collaboration with the SEM Fellowship program, a formal Sports Psychiatry rotation was created to ensure every Fellow had exposure to mental health assessment and management of athletes appropriate to what would be expected of SEM physicians. Results: A four-week curriculum which included direct clinical assessment, treatment initiation, follow up, and presentation of case-based mental health topics appropriate for SEM physicians. This program was reviewed during accreditation as part of the Enhanced Skills curriculum of the postgraduate Family Medicine program at McMaster University in 2023. Conclusion: An integrated Sports Psychiatry rotation during a SEM Fellowship provides an opportunity for future SEM physicians to have exposure to a clinical population of athletes with mental health concerns to develop tools for assessment, management, and collaborative care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".