Bringing complexity to sports injury prevention research: from simplification to explanation
Bibliographic record
Abstract
Sports injury prevention research takes being formulaic to the extreme. Countless papers begin by reminding that sports injuries remain a significant public health burden,1 and we are reassured that the proven efficacy of numerous interventions shows that sports injuries can be prevented.2 Despite this optimistic picture, and amidst the proliferation of consensus statements and guidelines, the effectiveness of sports injury prevention interventions remains disappointingly inconsistent. We trace these discrepancies to two approaches that have guided past work—simple and complicated—and then move to propose a potentially useful way forward, that of complexity. The ‘simple’ perspective advocates that injury incidence can be reduced via a recipe-type approach. Simplicity casts sports injuries as straightforward occurrences for which an optimal intervention is sought, where interventions either ‘work’ or ‘do not work’. The Sequence of Prevention model,3 for example, consists of four steps: (1) establish the extent of the problem, (2) establish the aetiology and extent of the injury, (3) introduce preventative measures and …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".