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Record W2300841360 · doi:10.1017/s1049023x00025577

Theme 8. Education Issues in Disaster Medicine: Summary and Action Plan

2001· article· en· W2300841360 on OpenAlexaff
Laurie Pearce, Linda B. Bourque, S.J. Armour, Peter Bastone, Marvin L. Birnbaum, Christopher Garrett, P. Gregg Greenough, C Manni, Norifumi Ninomiya, Jaime Renderos, Steven J. Rottman, Pardeep Sahni, Chung‐Liang Shih, David A. Siegel, Bradley N. Younggren

Bibliographic record

VenuePrehospital and Disaster Medicine · 2001
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVulnerability (computing)Psychological interventionAction (physics)Action planDisaster medicineTheme (computing)Plan (archaeology)Medical educationPublic relationsPsychosocialEngineering ethicsPolitical sciencePsychologyMedicineMedical emergencyPoison controlNursingEngineeringSuicide preventionComputer scienceComputer securityManagement

Abstract

fetched live from OpenAlex

INTRODUCTION: Change must begin with education. Theme 8 explored issues that need attention in Disaster Medicine education. METHODS: Details of the methods used are provided in the introductory paper. The chairs moderated all presentations and produced a summary that was presented to an assembly of all of the delegates. The chairs then presided over a workshop that resulted in the generation of a set of action plans that then were reported to the collective group of all delegates. RESULTS: Main points developed during the presentations and discussion included: (1) formal education, (2) standardized definitions, (3) integration, (4) evaluation of programs and interventions, (5) international cooperation, (6) identifying the psychosocial consequences of disaster, (7) meaningful research, and (8) hazard, impact, risk and vulnerability analysis. DISCUSSION: Three main components of the action plans were identified as evaluation, research, and education. The action plans recommended that: (1) education on disasters should be formalized, (2) evaluation of education and interventions must be improved, and (3) meaningful research should be promulgated and published for use at multiple levels and that applied research techniques be the subject of future conferences. CONCLUSIONS: The one unanimous conclusion was that we need more and better education on the disaster phenomenon, both in its impacts and in our response to them. Such education must be increasingly evidence-based.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.063
GPT teacher head0.419
Teacher spread0.356 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2001
Admission routes1
Has abstractyes

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