Factors associated with coach–athlete conversations about mental health in intercollegiate sport
Bibliographic record
Abstract
Increasing conversation about mental health is one way to decrease stigma and prevent/treat maladaptive processes within the context of sport. Due to their proximity and influence over athletes, coaches can increase conversation and change team culture. The objective of this study was to apply the Theory of Planned Behavior (TPB) to explore the factors associated with coach-athlete conversation about mental health. A total of 136 Canadian coaches completed a demographic questionnaire as well as a TPB-based survey measuring Attitudes, Perceived Behavioral Control (PBC), Social Norms and Intention. Intention was measured as Role perception, if a coach believed it was their role to be involved in athlete mental health. Behavior was measured as talking with an athlete(s) about mental health. Approximately 68% of coaches had spoken to athletes about mental health in the last season. The linear regression model predicted a significant amount (42.7%) of the variance in Intention ( p < .05) from the three TPB constructs. Logistic regression found a significant interaction effect of PBC and Intention on Behavior ( p < .01). Measured TPB construct scores were influenced by previous mental health training, personal experience with mental illness, age group and the act of talking ( p < .05). Although a promising amount of coaches spoke to athletes about mental health, improvement is still possible. Mental health training should continue to be promoted to all members of the athletic community. As attitude scores were generally positive, this training should potentially focus more on improving capabilities and social norms.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".