48 (20B) Impact of a massive open online course on knowledge about current and previous recommendations on sport-related concussions
Bibliographic record
Abstract
Purpose The objective of this study was to evaluate the impact of a massive open online course (MOOC) on sport-related concussion (SRC) as a KT strategy in terms of knowledge improvement and successful completion of the course.Methods A French language MOOC in concussion was updated to support the dissemination of the recommendations from the Amsterdam consensus from 2023. Self-registered participants had access for 8 weeks to modules on the following topics: introduction to concussion, prevention, detection, initial management, management of persistent symptoms and valid resources for updates. Successful completion rates and knowledge improvement were the primary outcomes. Pre- and post-intervention knowledge was assessed using a 15-question test. Five of these questions could be answered based on recommendations that remained unchanged since the Berlin consensus (2017) and 10 questions reflected changes following the Amsterdam consensus (2023).Results Of the 948 people that registered, 529 (56%) accessed the course at least once and 309 (33%) successfully completed the course. Participants included: 40% physiotherapists, 33% other health care providers, 27% other groups (sport, education, parents, etc.). The average pre- and post-course quiz scores improved from 48,4% to 75,8% (+27,4%). Performance on the 5 questions reflecting unchanged recommendation since Berlin improved by 15,0% (70,7% to 85,7%) compared to 33,5% (from 37,3% to 70,8%) for the recommendations that changed following the Amsterdam consensus.Conclusions These results further demonstrate that a MOOC is feasible and has a positive impact on concussion knowledge and support its use as a KT strategy for SRC.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.011 |
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".