Italian medical students and disaster medicine: Awareness and formative needs
Bibliographic record
Abstract
OBJECTIVE: Over the last century, the number of disasters has increased. Many governments and scientific institutions agree that disaster medicine education should be included in the standard medical curriculum. Italian medical students' perceptions of mass casualty incidents and disasters and whether-and if so to what extent-such topics are part of their academic program were investigated. DESIGN, SETTING, AND PARTICIPANTS: A Web-based survey was disseminated to all students registered with the national medical students' association (Segretariato Italiano Studenti Medicina), a member of the International Federation of Medical Students' Associations. The survey consisted of 14 questions divided into four sections. RESULTS: Six hundred thirty-nine medical students completed the survey; 38.7 percent had never heard about disaster medicine; 90.9 percent had never attended elective academic courses on disaster medicine; 87.6 percent had never attended non-academic courses on disaster medicine; 91.4 percent would welcome the introduction of a course on disaster medicine in their core curriculum; and 94.1 percent considered a knowledge of disaster medicine important for their future career. CONCLUSIONS: Most of the students surveyed had never attended courses on disaster medicine during their medical school program. However, respondents would like to increase their knowledge in this area and would welcome the introduction of specific courses into the standard medical curriculum.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".