Experience of Ethics Training and Support for Health Care Professionals in International Aid Wor
Bibliographic record
Abstract
Health care professionals who travel from their home countries to participate in humanitarian assistance or development work experience distinctive ethical challenges in providing care and services to populations affected by war, disaster or deprivation.Limited information is available about organizational practices related to preparation and support for health professionals working with non-governmental organizations.In this article, we present one component of the results of a qualitative study conducted with 20 Canadian health care professionals who participated in international aid work.The findings reported here relate to expatriate clinicians' experiences and perceptions of ethics preparation, training and support.The strategies examined include pre-departure training and preparation, in-field supports and retrospective debriefing of ethical issues.Participants experienced a range of training and supports as beneficial for addressing ethical challenges in humanitarian assistance and development work.Participants also expressed ambivalence or scepticism about the benefits offered by specific modalities.This analysis can contribute to informing discussions of how organizations and individual practitioners can best develop, implement and utilize ethics training and support for international aid work.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".