Concussion education in Canadian medical schools: a 5 year follow-up survey
Bibliographic record
Abstract
BACKGROUND: Despite concussion now being recognized as a public health priority in Canada, recent studies-including our 2012 survey of Canadian medical schools-have revealed major gaps in concussion education at the undergraduate medical school level. METHODS: We re-surveyed all 17 Canadian medical schools using a questionnaire divided in two categories: (1) concussion-specific education (2) head injury education incorporating a concussion component to determine whether there have been any improvements in concussion education at the medical school level during the last five years. For each year of medical school, respondents were asked to provide the estimated number of hours and teaching format for each category. RESULTS: We received replies from 13 of the 17 medical schools (76%). 11 of the 13 (85%) medical schools now reported providing concussion-specific education compared to 29% in our 2012 survey. The mean number of hours dedicated to category 1 learning in 2017 was 2.65 h compared to 0.57 in 2012, and the mean number of hours of category 2 increased to 7.5 from 1.54. CONCLUSION: Our follow-up study reveals increased exposure to concussion-related teaching in Canadian medical schools during the last five years. Persistent deficiencies in a minority of schools are highlighted. These should be addressed by reiterating the importance of concussion education for undergraduate medical students and by developing clear concussion-specific objectives at the national licensure level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".