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Record W2199897727

Disaster medicine education in Canadian medical schools before and after September 11, 2001.

2005· article· en· W2199897727 on OpenAlexaffabout
Garnet Cummings, Françesco Della Corte, Greta G. Cummings

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDisaster medicineFamily medicineEmergency managementMedical educationTerrorismSuicide preventionPoison controlMedical emergencyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.023
GPT teacher head0.357
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2005
Admission routes2
Has abstractyes

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