Athlete autonomy, supportive interpersonal environments and clinicians’ duty of care; as leaders in sport and sports medicine, the onus is on us: the clinicians
Bibliographic record
Abstract
Excellence for elite athletes demands painstaking attention to detail to all aspects of health, well-being and performance. Researcher and Olympic Taekwondo Gold Medalist Lauren Burns and coauthors1 use the power of the athlete story to argue strongly and convincingly that central to achieving excellence is durable interpersonal support. ‘If we look at an athlete as a whole person, there is a fundamental duty of care to ensure they are supported to become their best, most resilient self, both on and off the field. Athletes therefore need to be encouraged to seek interpersonal support that evolves as they move along their development pathway’. These sentences, both important, appear sequentially; but I will make one distinction—the onus to create a supportive environment should not rest primarily on athletes. Where then does the duty of care lie? According to Fisher et al ’s heuristic model,2 the power differential in sport particularly positions coaches to hurt or help their athletes, and as such coaches are responsible for athlete’s welfare. Those, …
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.022 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".