What do They Know? Guidelines and Knowledge Translation for Foreign Health Sector Workers Following Natural Disasters
Bibliographic record
Abstract
Introduction The incidence of natural disasters is increasing worldwide, with countries the least well-equipped to mitigate or manage them suffering the greatest losses. Following natural disasters, ill-prepared foreign responders may become a burden to the affected population, or cause harm to those needing help. Problem The study was performed to determine if international guidelines for foreign workers in the health sector exist, and evidence of their implementation. METHODS: A structured literature search was used to identify guidelines for foreign health workers (FHWs) responding to natural disasters. Analysis of semi-structured interviews of health sector responders to the 2015 Nepal earthquake was then performed, looking at preparation and field activities. RESULTS: No guidelines were identified to address the appropriate qualifications of, and preparations for, international individuals participating in disaster response in the health sector. Interviews indicated individuals choosing to work with experienced organizations received training prior to disaster deployment and described activities in the field consistent with general humanitarian principles. Participants in an ad hoc team (AHT) did not. CONCLUSIONS: In spite of need, there is a lack of published guidelines for potential international health sector responders to natural disasters. Learning about disaster response may occur only after joining a team. Dunin-Bell O . What do they know? Guidelines and knowledge translation for foreign health sector workers following natural disasters. Prehosp Disaster Med. 2018;33(2):139-146.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".