Domestic Application of Lessons Learned by Canadian Healthcare Professionals Working in International Disaster Settings
Bibliographic record
Abstract
Context: The potential for disasters and public health emergencies is increasing globally, requiring expertise and capacity to mitigate and manage such events. Individuals with prior international disaster response represent an essential source of expertise to support disaster response in their home countries. Objective: The objective of this study was to explore the experiences of healthcare personnel involved in international emergency response regarding their perceptions of the individual attributes and collective team capacities during disaster response and how this may benefit disaster management in Canada. Study Design and analysis: This descriptive qualitative research study consisted of semi-structured interviews with key informants. Interviews were audio-recorded and transcribed. Conventional content analysis was performed on the transcripts, and themes were developed. Setting: Interviews were either conducted over the phone or in person. Population studied: Participants were delegates involved with the Canadian Red Cross medical response team, and who acted in a clinical or technical (physicians, surgeons, nurses) or administrative role (team leaders, administrators) within the last 5 years. Instrument: Semi structured interview guide was designed and piloted. It included open ended questions that explored participants’ experience on medical emergency response team and related lessons learned in terms of skills and capacities acquired. Outcome measures: Participant narratives of individual skills and team capacities acquired during disaster response and how these skills and capacities can be applied domestically in Canada. Results: Eighteen key informants provided perspectives on individual attributes acquired during international deployments, (ex; agility, stress management), and team capacities developed, including collaboration and conflict management. Key informants included nurses, physicians, technicians, and administrators and described these experiences as highly relevant to the domestic context. Conclusions: Canadian physicians and healthcare workers involved with international disaster response have already acquired essential capacities, and this experience can be vital to building efficient disaster response teams. These findings complement the CanMEDS roles and can inform course design, competency and curriculum development for family medicine and primary care professional training programs related to disaster response.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".