Models for Humanitarian Health Care Ethics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.139 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.048 |
| 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 it