Pathways through acute athlete care during training and major tournaments: a multi-national conceptualised process
Bibliographic record
Abstract
There have been growing discussions across international societies since 2017 focused on athlete wellness and athlete care. The human condition of high-performance athletes requires life balance, holistic personhood, and a functional athletic career with the support of integrative resources from sport organisations. During two successive International Society of Sport Psychology Think Tanks on Athlete Mental Health in 2018 and 2019, an international group of practitioners from Olympic and professional sport organisations discussed topics spanning what athlete mental health should look like, while problematising an overly narrow focus on athlete mental ill-health (i.e., an unbalanced approach to the topic), how it is being diagnosed, and how it is understood through research. Discussions have advanced into structural suggestions regarding standards of care for athletes in their daily training environments and at major international tournament events. Within this consensus statement, the authors focus our discussions onto athlete acute care. Emphasis is placed on how an integrated support team can work efficiently with high-performance athletes when acute care is required in two general contexts: (1) within the training environment, and (2) onsite at major events. A model is proposed to spur discussions and better standards to guide the athlete acute care process. Recommendations are provided for sport psychology practitioners, researchers, and high-performance sport organizations.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".