The ideal of equal health revisited: definitions and measures of inequity in health should be better integrated with theories of distributive justice
Bibliographic record
Abstract
The past decade witnessed great progress in research on health inequities. The most widely cited definition of health inequity is, arguably, the one proposed by Whitehead and Dahlgren: "Health inequalities that are avoidable, unnecessary, and unfair are unjust." We argue that this definition is useful but in need of further clarification because it is not linked to broader theories of justice. We propose an alternative, pluralist notion of fair distribution of health that is compatible with several theories of distributive justice. Our proposed view consists of the weak principle of health equality and the principle of fair trade-offs. The weak principle of health equality offers an alternative definition of health equity to those proposed in the past. It maintains the all-encompassing nature of the popular Whitehead/Dahlgren definition of health equity, and at the same time offers a richer philosophical foundation. This principle states that every person or group should have equal health except when: (a) health equality is only possible by making someone less healthy, or (b) there are technological limitations on further health improvement. In short, health inequalities that are amenable to positive human intervention are unfair. The principle of fair trade-offs states that weak equality of health is morally objectionable if and only if: (c) further reduction of weak inequality leads to unacceptable sacrifices of average or overall health of the population, or (d) further reduction in weak health inequality would result in unacceptable sacrifices of other important goods, such as education, employment, and social security.
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.
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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.076 |
| Scholarly communication | 0.015 | 0.042 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".