Canadian University sport coaches’ experiences participating in a social learning space on topics related to and impacting coach mental health.
Bibliographic record
Abstract
Although high-performance coaching can be rewarding, it can also cause considerable performance, organizational, and personal stress, which over time, can diminish coaches' mental health. There is a growing recognition that collective and community-based interventions that extend beyond the individual level are needed to support mental health, such as a social learning space. Therefore, the purpose of this study was to understand university coaches' experiences participating in a season-long social learning space on topics related to and impacting coach mental health. Specifically, 10 Canadian University coaches engaged in six monthly sessions throughout one competitive season on topics related to mental health. Data collection consisted of pre- and postseason individual, semistructured interviews. Data were analyzed abductively using a reflexive thematic analysis. Results revealed that coaches reported positive experiences from participating in the social learning space, including the sense of community it created and the quality of the knowledge provided by the invited speakers. The environment encouraged sharing and provided insights into mental health, leadership, and the coach-athlete relationship. Coaches also acquired new resources, improved personal well-being, and enhanced coaching effectiveness. Overall, this initiative empowered coaches to become advocates for mental health-both for themselves and for their athletes and networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".