Just compassion: implications for the ethics of the scarcity paradigm in clinical healthcare provision
Bibliographic record
Abstract
Primary care givers commonly interpret shortages of time with patients as placing them between a rock and a hard place in respect of their professional obligations to fairly distribute available healthcare resources (justice) and to offer a quality of attentive care appropriate to patients' states of personal vulnerability (compassion). The author argues that this a false and highly misleading conceptualisation of the basic structure of the ethical dilemma raised by the rationing of time in clinical settings. Drawing on an analysis of the Aristotelian virtue of nemesis, or "the sense of justice", it is argued that, far from being a moral orientation distinct from justice, compassion is a justice response insofar as it is conceptualised as a rational, appropriate response to others' adversity. The author then proceeds to point out that the perspective on justice and compassion as attributes of healthcare professionalism suggests a novel critical viewpoint on the ethics of managed forms of clinical rationing and the "scarcity paradigm" they engender: clinical conditions where primary care givers' time is intentionally rendered a commodity in chronically short supply run a deficit of justice, because they fail to make adequate accommodations for the provision of the quality of care human beings deserve in situations of illness and injury, and when they are dying.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.125 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".