Implementation of the International Olympic Committee Sport Mental Health Assessment Tool 1: Screening for Mental Health Symptoms in a Canadian Multisport University Program
Bibliographic record
Abstract
OBJECTIVE: To apply the International Olympic Committee Sport Mental Health Assessment Tool 1 (SMHAT-1) to determine the prevalence of mental health symptoms in a cohort of university student athletes over an academic year. A secondary objective was to explore the internal consistency of the screening tools from the SMHAT-1. DESIGN: Cross-sectional design with 3 repeated measurements over an academic year. SETTING: A large university multisport program. PARTICIPANTS: Five hundred forty-two university-level student athletes from 17 sports. INTERVENTION: N/A. MAIN OUTCOME MEASURES: On 3 occasions, the participants completed the SMHAT-1, which consists of the Athlete Psychological Strain Questionnaire. If an athlete's score was above the threshold (≥17), the athlete completed step 2, consisting of (1) Generalized Anxiety Disorder-7; (2) Patient Health Questionnaire-9; (3) Athlete Sleep Screening Questionnaire; (4) Alcohol Use Disorders Identification Test Consumption; (5) Cutting Down, Annoyance by Criticism, Guilty Feeling, and Eye-openers Adapted to Include Drugs; and (6) Brief Eating Disorder in Athletes Questionnaire. Internal consistency of the SMHAT-1 was also measured. RESULTS: Participants reported mental health symptoms with prevalence of 24% to 40% for distress, 15% to 30% for anxiety, 19% to 26% for depression, 23% to 39% for sleep disturbance, 49% to 55% for alcohol misuse, 5% to 10% for substance use, and 72% to 83% for disordered eating. Female athletes were more likely to suffer psychological strain, depression, and sleep disturbance; male athletes were more likely to report substance use. CONCLUSIONS: The SMHAT-1 was feasible to implement with good internal consistency. University-level athletes suffer from a variety of mental health symptoms underscoring the necessity for team physicians to have the clinical competence to recognize and treat mental health symptoms.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".