From consensus to action: knowledge transfer, education and influencing policy on sports concussion
Bibliographic record
Abstract
OBJECTIVE: To: (1) provide a review of knowledge transfer (KT) and related concepts; (2) look at the impact of traditional and emerging KT strategies on concussion knowledge and education; (3) discuss the value and impact of KT to organisations and concussion-related decision making and (4) make recommendations for the future of concussion education. DESIGN: Qualitative literature review of KT and concussion education literature. INTERVENTION: PubMed, Medline and Sport Discus databases were reviewed and an internet search was conducted. The literature search was restricted to articles published in the English language, but not restricted to any particular years. Altogether, 67 journal articles, 21 websites, 1 book and 1 report were reviewed. RESULTS: The value of KT as part of concussion education is increasingly becoming recognised. Target audiences benefit from specific learning strategies. Concussion tools exist, but their effectiveness and impact require further evaluation. The media is valuable in drawing attention to concussion, but efforts need to ensure that the public is aware of the right information. Social media as a concussion education tool is becoming more prominent. Implementation of KT models is one approach which organisations can use to assess knowledge gaps; identify, develop and evaluate education strategies and use the outcomes to facilitate decision-making. CONCLUSIONS: Implementing KT strategies requires a defined plan. Identifying the needs, learning styles and preferred learning strategies of target audiences, coupled with evaluation, should be a piece of the overall concussion education puzzle to have an impact on enhancing knowledge and awareness.
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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 | 0.135 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".