A longitudinal examination of changes in mental health among elite Canadian athletes
Bibliographic record
Abstract
This study explored how athletes’ symptoms of mental disorders changed over the course of pandemic year. Predictors of baseline levels and changes in symptoms of mental disorders were also examined. Surveys were completed four times throughout a year by Canadian athletes training for the 2020 Olympics/Paralympics (ntime1 = 186, ntime2 = 142, ntime3 = 123, ntime4 = 108). Surveys included demographics questions, measures of perceived stress, training load, social support, coping, self-esteem, depression, anxiety, and disordered eating. Data were analysed using descriptive statistics and latent growth modelling. The prevalence of mental disorder symptoms was high at baseline and there was no significant change over time. Scores for the three disorders were significantly correlated. Female athletes had higher scores for disordered eating at baseline. Higher levels of perceived stress predicted higher scores on mental disorder measures. Longitudinal tracking of symptoms of mental disorders among elite athletes is important because it allows researchers to explore whether disorder symptomologies change; rates of mental disorder symptoms were high at baseline and stayed high over the course of a year. More research is needed to explore possible gender differences in rates of disorder symptoms, and to understand how those symptoms change over time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".