Disaster medicine education in Canadian medical schools before and after September 11, 2001.
Bibliographic record
Abstract
OBJECTIVE: To describe disaster medicine (DM) education in 16 Canadian medical schools before and after September 11, 2001 (9/11). METHODS: Email invitations and reminders to complete an Internet-based survey were sent to 48 undergraduate and fellowship representatives. RESULTS: A total of 24 responses were received from 15 of the 16 Canadian medical schools in operation at the time of the study, representing 10 undergraduate and 14 fellowship programs. Prior to 9/11, 22 programs at 9 schools taught DM compared with 14 programs post 9/11, a reduction of 37%. Six schools reported no DM teaching before 9/11; 7 reported no DM instruction after that date. Respondents from 12 schools felt that DM should be taught at the undergraduate level, and 9 of the 12 felt it should be included as core content. Respondents from all 15 responding schools felt that DM should be included as core content at the fellowship level. Twenty-two respondents (92%) indicated a belief that the public expects physicians to be prepared to deal with the consequences of disasters. The most frequently taught topics were emergency medical services and disasters, disaster management, hospital disaster planning, and bioterrorism. CONCLUSION: Despite support for DM instruction and increases in terrorism and global disasters, 46% of the responding medical schools do not teach this topic and there has been a downward trend in this regard since 9/11.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".