‘Playing God Because you Have to’: Health Professionals ’ Narratives of Rationing Care in Humanitarian and Development Work
Bibliographic record
Abstract
This article explores the accounts of Canadian-trained health professionals working in humanitarian and devel-opment organizations who considered not treating a patient or group of patients because of resource limita-tions. In the narratives, not treating the patient(s) was sometimes understood as the right thing to do, and sometimes as wrong. In analyzing participants ’ narratives, we draw attention to how medications and equip-ment are represented. In one type of narrative, medications and equipment are represented primarily as scarce resources; in another, they are represented as patient care. In the contexts where our respondents were work-ing, medications and equipment were often both patient care interventions and scarce resources. The analytic point is that health professionals tend to emphasize one conceptualization over the other in coming to assert that not treating is right, or wrong. Rendering tacit ethical frameworks more explicit makes them available for reflection and debate. Priority setting is a persistent challenge in health care. Much attention is devoted to assembling evidence and tools for planners charged with distributing limited re-sources across broad needs. Yet, questions of who gets what, on what basis, necessarily implicate social values
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.045 | 0.081 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".