Knowledge transfer principles as applied to sport concussion education
Bibliographic record
Abstract
OBJECTIVE: To (a) examine knowledge transfer literature and optimal learning needs as applied to healthcare professionals, coaches and student athletes; (b) apply the practice of knowledge transfer to sport concussion education resources; and (c) identify needs and make recommendations for optimising concussion education. DESIGN: Qualitative literature review of knowledge transfer and concussion education literature. INTERVENTION: Pubmed, Medline, Psych Info and Sport Discus databases were reviewed. 52 journal articles, 20 websites and 2 books were reviewed. RESULTS: The methods in which individuals experience optimal learning varies and should be considered when developing effective concussion education strategies. Physician knowledge and performance are impacted by education outreach, interaction and reminder messages. Educational strategies associated with optimal learning for physio and athletic therapists include problem and evidence-based practice, socialisation and peer-assisted learning. From a coaching perspective, research supports the reflective process as a learning modality. Student athletes have strengths and weaknesses in different areas and so perform differently on activities requiring distinct strategies. Knowing the impact of sport concussion resources on knowledge enhancement and modifying attitudes and behaviours toward concussion requires evaluation strategies. Review of concussion resources using the perspective of knowledge transfer and methods for improvement is discussed. CONCLUSIONS: Knowledge transfer is a relatively new concept in sports medicine and its influence on enhancing concussion education is not well known. The needs and optimal learning styles of target audiences coupled with evaluation need to be a piece of the overall concussion education puzzle to effectively impact knowledge of and attitudes and behaviours towards sport concussion.
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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 | 0.067 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".