Models for Humanitarian Health Care Ethics
Bibliographic record
Abstract
Humanitarian health care practitioners working outside familiar settings, and without familiar supports, encounter ethical challenges both familiar and distinct. The ethical guidance they rely upon ought to reflect this. Using data from empirical studies, we explore the strengths and weaknesses of two ethical models that could serve as resources for understanding ethical challenges in humanitarian health care: clinical ethics and public health ethics. The qualitative interviews demonstrate the degree to which traditional teaching and values of clinical health ethics seem insufficient for addressing all the realities of health care practice during humanitarian missions. They equally suggest that greater good orientations of public health ethics can thwart the best intentions of health care professionals wanting to attend to the interests of individual patients. Even though neither is complete on its own for helping guide health professionals on field missions, taken together these models have much to offer. At the same time, the narratives of the humanitarian health care workers illustrate how some of the crucial differences between public health ethics and clinical ethics generate tensions in humanitarian health practice. We offer an analysis of some of the complexities this creates for humanitarian health care ethics, and consider ways of adjudicating between the two models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".