(A113) Ethics in the Delivery of Humanitarian Health Response: Learning from the Narratives of Health Care Workers
Bibliographic record
Abstract
Aims 1. To hear the types of ethical challenges foreign health care workers (HCWs) experience while providing health care in conditions of disaster and deprivation. 2. To hear how they responded 3. To understand the kinds of resources that may have been helpful to support HCWs in these ethical dilemmas. Methods Qualitative study, loosely grounded theory. Canadian trained HCWs (n = 20, mean age 39) who have worked in disaster response, conflict, post disaster. Results Ethical dilemmas emerged from 4 main sources: resource scarcity, historical/political/social structures, aid agency policies/agendas, HCWs norms roles/interactions. Participants described little preparation to deal with ethical dilemmas, and the value in pre-departure training. Clinicians are nurtured in western ethics- mostly formed on autonomy, beneficence, non-maleficence and justice. New realities for many were related to community oriented Public Health Ethics. Early discussion has emerged about the possibility of developing a simple, practical, hand held decision-making model (toolkit) to be used in the field to help guide reflection about ethical dilemmas for HCWs in disaster settings.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".