The impact of an online interprofessional course in disaster management competency and attitude towards interprofessional learning
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".