A Knowledge Translation Framework for Improving Concussion Education Among Medical Students and Physicians
Bibliographic record
Abstract
Objective: The objective of this paper is to outline key principles required for a knowledge translation (KT) strategy on concussion education for medical trainees and physicians to promote knowledge retention and practice change. Design: Qualitative review of the literature on concussion education for medical trainees and physicians utilizing the Canadian Institute of Health Research (CIHR) Knowledge to Action (KTA) Cycle as a framework. Results: Medical education on concussion appears to be increasing, but many knowledge gaps persist. Although many concussion guidelines and standardized assessments have been developed, many physicians are either not aware of them, do not use them, or provide inaccurate or inconsistent discharge instructions. Focused, interactive concussion education sessions, education outreach by trained facilitators, and adoption of a spiral curriculum are preferred modalities. To facilitate concussion education, medical professionals must recognize the importance of concussion in their practice. Interventions should deliver high-yield information and be integrated into existing programs such as academic half days (AHD) and the Maintenance of Certification Program (MOC). Many KT tools and interventions have been developed, such as the Concussion Awareness Training Tool (CATT) for Medical Professionals, but evidence of their utilization and effectiveness is limited. Existing tools should be reviewed, updated, implemented, and evaluated for their effectiveness of improving both conceptual and instrumental knowledge. Conclusion: KT strategies for concussion medical education should utilize the CIHR KTA Cycle principles outlined in this review as a guide to design interventions that improve the concussion knowledge of medical trainees and physicians.
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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 | 0.055 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".