Development of Australian mental health guidelines for community sport
Bibliographic record
Abstract
OBJECTIVE: The need for clear and actionable guidelines for the promotion and protection of mental health in organised community sport has previously been identified. This study aimed to provide guidelines to promote and protect mental health in organised community sport in Australia. METHODS: Guideline development was informed by (1) systematic reviews of the evidence pertaining to existing mental health guidelines in sport and mental health interventions in community sport; (2) an expert Delphi consensus study and (3) key stakeholder input via focus groups. A Guideline Development Committee comprising experts and key stakeholder representatives articulated nine distinct guidelines. RESULTS: These guidelines address the areas of: mental health literacy training; mental health support pathways and processes; responding to mental health emergencies; responding to major events that may impact mental health; having a mental health plan in place; reducing stigmatising attitudes; appointing a dedicated mental health champion; coach education and promoting well-being within the organisation. CONCLUSIONS: We provide guidance for promoting and protecting mental health in community sport. Monitoring uptake and measuring the effectiveness of the guidelines are important areas of future work to advance positive mental health for everybody involved in community sport.
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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 | 0.064 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".