The role of high-performance sport environments in mental health: an international society of sport psychology consensus statement
Bibliographic record
Abstract
This consensus statement is the product of the Third International Society of Sport Psychology Think Tank on Mental Health. The purposes of the Think Tank were (1) to engage renowned international expert researchers and practitioners in a discussion about the role of high-performance sport environments in nourishing or malnourishing the mental health of athletes, coaches and staff; and (2) to develop recommendations for sport organisations, mental health researchers, and practitioners to more fully recognise the role of the sport environment in their work. Although most of the research on mental health in sport has focused on the individual, mental health is the result of intricate and dynamic relationships between people and their environments, and a range of stakeholder individuals and organisations play a key role in supporting wellbeing in high-performance sport. We conceptually divide the environment into three levels (the sport team, sport organisation and sport system) and two dimensions (the social and the physical environment). Based on the portraits of these environments, we conclude by providing recommendations that will help sport teams, organisations, and systems to create nourishing high-performance sport environments and effective mental health service provision environments, whilst helping researchers expand their focus from the individual athlete or coach to the sport environment.
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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 | 0.129 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.024 | 0.042 |
| Insufficient payload (model declined to judge) | 0.005 | 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".