Bibliographic record
Abstract
Research on mental health has flourished in recent years (e.g., Vella et al., 2021), however, much of the research in this area has relied on cross-sectional designs, and there is limited work studying mental disorder among Canadian athletes. Research is also needed to understand the effect that Canadian sport environments can have on athletes’ mental health. The purpose of this dissertation was to explore the mental health experiences of elite Canadian athletes. Studies One and Two evaluated the prevalence of symptoms of mental disorders (depression, anxiety, and eating disorders) among elite Canadian athletes, and explored how athletes’ symptoms of mental disorders changed over the course of a pandemic year. Data were collected through surveys that were completed at four times by elite Canadian athletes (ntime1 = 186, ntime2 = 142, ntime3 = 123, ntime4 = 108). Study One relied on data from time one, while Study Two relied on data from all four time points. Data were analyzed using descriptive statistics, correlations, and latent growth modeling. Symptoms of mental disorder were very prevalent among this population at baseline (~40%), and did not change over time. Stress and training load were correlated with symptoms of disorder, and there were some differences in the prevalence and changes in disorder symptomology over time between men and women. The purpose of Study Three was to understand how elite sport training environments in Canada impact the mental health of the athletes who train within them. Thirty-two Canadian athletes participated in one semi-structured interview. Data were analyzed using thematic analysis. Sport environments played a role in athletes’ perceptions of stigma and their willingness to seek help which impacted their overall mental health experiences. Together, these studies provide a more complete picture of Canadian athletes’ mental health and may inform interventions and policies designed to support athlete mental health.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".