Knowledge translation in sport injury prevention research: an example in youth ice hockey in Canada
Bibliographic record
Abstract
There is a critical need for scientists to incorporate a knowledge translation (KT) perspective into research plans to demonstrate the relevance of research findings and evaluate their implications for health practice and policy. Since 2011, the British Journal of Sport Medicine ( BJSM ) has had a focus on implementation and dissemination research.1 This field is consistent with KT, which is the term used by the Canadian Institutes of Health Research (CIHR). As the following research example was conducted in Canada, the terminology KT is used, acknowledging similarities to implementation and dissemination concepts referred to elsewhere in BJSM . Using an interdisciplinary approach, the knowledge exchange process should influence healthcare professionals, community members and other decision-making groups. On the basis of the original model developed by van Mechelen et al ,2 injury prevention research in sport includes identification of injury burden, examination of risk factors, and development, implementation and evaluation of prevention strategies to reduce injury risk. As sport injury prevention programmes cannot be impactful without acceptance and adoption by targeted individuals, an extension of this model must include real-world implementation contexts and evaluation of their effectiveness in a broader, ecological context (figure 1).3 Figure 1 Sport Injury Prevention Research Centre adapted integrated knowledge translation model. The prevention of injuries and their long-term …
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".