Determining the role of sport coaches in promoting athlete mental health: a narrative review and Delphi approach
Bibliographic record
Abstract
BACKGROUND: Coaches have the potential to support athlete mental wellness, but many are unsure what to do and concerned they may unintentionally engage in behaviours that negatively impact their athletes. Education has the potential to help coaches engage in primary, secondary and tertiary preventive behaviours related to athlete mental health; however, there exists no empirical or consensus basis for specifying the target behaviours that should be included in such education. OBJECTIVE: The aim of this research was to review extant literature about the role of sport coaches in mental health prevention and promotion, and obtain expert consensus about useful, appropriate and feasible coach behaviours. DESIGN: Modified Delphi methodology with exploration (ie, narrative review) and evaluation phase. DATA SOURCES: Twenty-one articles from PubMed, PsycINFO and ProQuest, and grey literature published by prominent sport organisations. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: All studies were English-language articles that focused on the role of coaches as they relate to (1) culture setting in sport, (2) addressing athlete mental health and (3) providing ongoing support to athletes with mental health concerns. No study design, publication date limits or sport characteristics were applied. RESULTS: The coach's role should include fostering team cultures that support athlete mental health, encouraging care-seeking and supporting athletes currently receiving mental healthcare. SUMMARY/CONCLUSION: The behaviours specified herein have implications for coach education programme development. This study is the first to use a structured Delphi process to develop specific recommendations about the role coaches can play in supporting 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 | 0.090 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| 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".