Physicians’ Responses to Resource Constraints
Bibliographic record
Abstract
BACKGROUND: A common dilemma that confronts physicians in clinical practice is the allocation of scarce resources. Yet the strategies used by physicians in actual situations of resource constraint have not been studied. This study explores the strategies and rationales reported by physicians in situations of resource constraints encountered in practice. METHODS: A national survey of US internists, oncologists, and intensive care specialists was performed by computer-assisted telephone interviews. As part of this survey, we asked physicians to tell us about a recent ethical dilemma encountered in practice. A subset of respondents reported difficulties regarding resource allocation. Transcripts of open-ended responses were coded for content based on consensus. RESULTS: Of the 600 physicians originally identified, 537 were eligible and 344 participated (response rate, 64%). Internists do not make allocation decisions alone but rather engage in negotiation in their resolution. Furthermore, these decisions are not made as dichotomous choices. Rather they often involve alternative solutions in the face of complexities of both the health care system and situations where limited resources must be allocated. Justice is not commonly the justification for rationing. CONCLUSION: Physicians' experiences in situations of resource constraints appear to be more complex than the normative literature on health care rationing assumes. In addition, reasoning about justice in health care seems to play only a small part in clinical decision making. Bridging this gap could be an important step in fostering fair allocation of resources in difficult cases.
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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 | 0.015 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".