Aid when there is “nothing left to offer”: A study of ethics & palliative care during international humanitarian action
Bibliographic record
Abstract
In humanitarian crises such as disaster, epidemic, conflict or extreme deprivation, triage approaches tend to direct limited resources to those most likely to improve and survive (eHospice 2015). Standards of clinical care for crisis settings emphasize that patients who are dying should be treated with respect, accompanied (Institute of Medicine 2009), and provided with pain relief (WMA 2006). Where demands for care dramatically outweigh resources, however, dying patients may be left unattended, or attended by health care providers (HCPs) who do not know what to do for them, or worse, who treat patients as if they were already dead (Orbinsky 2008). The premise of this study is that following sudden onset disaster, epidemic, or during protracted armed conflict, humanitarian teams often operate in high mortality settings, raising crucial questions about care for the incurable and the dying, their families, and communities. We aim to better understand ethical and practical experiences, challenges, and possibilities related to the integration of palliative care in the response of humanitarian organizations in different crisis situations. https://humanitarianhealthethics.net/hhe-research-studies/ https://www.elrha.org/project/ethics-palliative-care-international-humanitarian-action/
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".