Injury prevention in medical education: a Canadian medical school survey
Bibliographic record
Abstract
BACKGROUND: Injury has a major societal impact. In Canada, injury is the leading cause of death among those aged 1-44 years, the fifth-leading cause of death among those of all ages and is responsible for a burden of US$26.8 billion in 2010. It holds that most injuries are predictable and preventable, and therefore, such statistics represent a serious public health concern. Given that physicians play a vital role in the prevention and control of injuries, further information regarding the current state of injury prevention education in medical undergraduate programmes in Canada would be beneficial. We hypothesise that the results of an observational survey distribute to all Canadian medical schools will demonstrate a substantial gap in injury prevention education integration in the existing medical school curriculums. STUDY OBJECTIVE: To evaluate the current status of Injury Prevention Education in Canadian Medical Schools preclerkship and clerkship medical curriculum. METHODS: Electronic surveys evaluating the current status of injury prevention education were sent via email to each of the 16 Canadian medical schools. RESULTS: Nine Canadian medical faculties (56%, n=9) responded. Eight of the nine medical schools (88.89%, n=8) offered at least five injury prevention related topics in their respective curricula. The most common injury-related courses were Role of physicians in the prevention of injuries (100%, n=9) and epidemiology of injury (88.89%, n=8). All respondent medical faculties (100%, n=9) offered at least a single injury prevention specific topic in their curricula. Most surveyed medical faculties (88.89%, n=8) offered nine injury-specific topics. The most common injury-specific topics included falls, suicide and self-harm, alcohol, burns and scalds, and concussion (100%, n=9).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".