Could a massive open online course be part of the solution to sport-related concussion? Participation and impact among 8368 registrants
Bibliographic record
Abstract
OBJECTIVES: A massive open online course (MOOC) has the potential to help address the public health burden of concussion across all levels of sport and leisure activities. The main objectives of this study were to document the volume of participation and to estimate the impact of a MOOC on concussion protocol implementation. METHODS: Between April 2016 and October 2018, four editions of a French-language MOOC on concussion were presented. Each of the six modules contains a section presenting the main learning content and a section proposing a reflective process to support the implementation of a concussion protocol in the participant's environment. The proportion of registrants who achieved successful completion of the course was the main outcome. Surveys were also used to document the types of participants and their intent to implement or update a protocol. RESULTS: Thirty per cent of 8368 registrants successfully completed the course. Of the 3061 participants who completed a survey about their background, 58.8% were healthcare professionals, 16.3% were sport or school stakeholders, and 10.1% were parents or persons who sustained a concussion. Of the 1471 participants who completed a survey about their intent to implement or update a concussion protocol in their environment, 39.4% answered positively. CONCLUSION: This study describes the first use of a MOOC to address the issue of concussion. The experience of a French-language MOOC shows promising results supporting the use of this innovative educational strategy as part of the solution to the public health issue of 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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".