Advancing adherence research in sport injury prevention
Bibliographic record
Abstract
Have you ever wondered why some patients do not adhere to drug prescriptions despite warnings regarding the health consequences of non-adherence? The simple reason is that it takes more than just a prescription and education to get patients to take their drugs. A similar scenario has become apparent in the field of sport injury prevention. Over the past two decades, sport injury prevention researchers have developed innovative and proven interventions for injury prevention in athletes. However, most interventions have been developed without the optimal implementation context in mind. Researchers provide evidence of intervention efficacy and as much public advocacy as possible, more like the ’prescribe and educate’ tradition. Unfortunately, the challenge of non-adherence remains palpable. The WHO defines adherence as ‘the extent to which a person’s behaviour – taking medication, following a diet, and/or executing lifestyle changes – corresponds with agreed recommendations from a healthcare provider’.1 The effectiveness of any treatment or prevention intervention is determined jointly by its efficacy and user adherence to the intervention. While it is common practice for ‘compliance’ and ‘adherence’ to be interchangeably used by researchers, these constructs have different meanings.1 2 Adherence has been identified …
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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 | 0.050 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".