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Record W2153221147 · doi:10.3109/13561820902886238

The impact of an online interprofessional course in disaster management competency and attitude towards interprofessional learning

2009· article· en· W2153221147 on OpenAlexafffundabout
Lynda Atack, Kathryn Parker, Marie Rocchi, Janet Maher, Trish Dryden

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoMichener InstituteCentennial College
FundersUniversity of TorontoBrown University
KeywordsMedical educationCurriculumInterprofessional educationEmergency managementProfessional developmentPsychologyHealth careNursingMedicinePedagogyPolitical science

Abstract

fetched live from OpenAlex

A recent national assessment of emergency planning in Canada suggests that health care professionals are not properly prepared for disasters. In response to this gap, an interprofessional course in disaster management was developed, implemented and evaluated in Toronto, Canada from 2007 to 2008. Undergraduate students from five educational institutions in nursing, medicine, paramedicine, police, media and health administration programs took an eight-week online course. The course was highly interactive and included video, a discussion forum, an online board game and opportunity to participate in a high fidelity disaster simulation with professional staff. Curriculum developers set interprofessional competency as a major course outcome and this concept guided every aspect of content and activity development. A study was conducted to examine change in students' perceptions of disaster management competency and interprofessional attitudes after the course was completed. Results indicate that the course helped students master basic disaster management content and raised their awareness of, and appreciation for, other members of the interdisciplinary team. The undergraduate curriculum must support the development of collaborative competencies and ensure learners are prepared to work in collaborative practice.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.023
GPT teacher head0.466
Teacher spread0.443 · 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 designObservational
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

Citations54
Published2009
Admission routes3
Has abstractyes

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