Disaster medicine education for physicians: a systematic review
Bibliographic record
Abstract
Introduction: Events such as September 11, 2001, the 2005 tsunami in southeast Asia, and hurricane Katrina in the United States have emphasized the necessity for disaster medicine education in medical schools internationally. Society expects that physicians will be capable of planning for and managing the consequences to mankind of natural and man‐made disasters. Objective: The purpose of this systematic review was to examine articles related to disaster medicine in indexed peer‐reviewed journals, describing courses for medical students, physicians and medical military, before and after September 11, 2001. Method: This was a systematic review, from 1985 to 2006, in the English language, of four bibliographic databases (ERIC, MEDLINE, Embase, and Healthstar). Methodological quality assessment of courses described in the included articles was completed using the Learning Outcomes Inventory (LOI), which was developed to assess four key components for managing medical education: course objectives, course content, evaluation process, and target audience. Results: The initial search yielded 7595 research titles. With increasing specificity in inclusion and exclusion criteria, 54 articles (34 qualitative/20 quantitative) were retained, with 26 published before September 11, 2001 and 28 after. All articles were evaluated against the criteria from the LOI, resulting in 25 articles graded as weak, 25 as moderate, and 4 as strong. Conclusion: The body of knowledge in indexed peer‐reviewed journals concerning disaster medicine curriculum was limited in quantity and quality before September 11, 2001, but has improved dramatically since. This increase in quality and quantity of published articles is promising in view of the plethora of web‐based reports describing disaster medicine courses that have not been indexed or peer‐reviewed.
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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 | 0.013 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".