Domestic application of lessons learned by Canadian health care professionals working in international disaster settings: a qualitative research study
Bibliographic record
Abstract
<h3>Background:</h3> Individuals with prior experience in international disaster response represent an essential source of expertise to support disaster response in their home countries. Our objective was to explore the experiences of personnel involved in international emergency health response regarding their perceptions of essential disaster response attributes and capacities and determine how these competencies apply to the Canadian context. <h3>Methods:</h3> For this qualitative study, we conducted semistructured interviews with key informants in person or over the telephone from May to December 2018. Participants were delegates deployed as part of the Canadian Red Cross medical response team in a clinical or technical, or administrative role within the last 5 years. Interviews were audio-recorded and transcribed. Conventional content analysis was performed on the transcripts, and themes were developed. <h3>Results:</h3> Eighteen key informants from 4 Canadian provinces provided perspectives on individual attributes acquired during international deployments, such as agility and stress management, and team capacities developed, including collaboration and conflict management. Key informants, including administrators (<i>n</i> = 5), technicians (<i>n</i> = 4), nurses (<i>n</i> = 4), physicians (<i>n</i> = 3) and psychosocial support workers (<i>n</i> = 2), described these experiences as highly relevant to the Canadian domestic context. <h3>Interpretation:</h3> Canadian physicians and health care workers involved with international disaster response have already acquired essential capacities, and this experience can be vital to building efficient disaster response teams in Canada. These findings complement the Canadian Medical Education Directives for Specialists (CanMEDS) roles and can inform course design, competency and curriculum development for physician and professional training programs related to disaster response and preparedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".