Bibliographic record
Abstract
BACKGROUND: Health inequality has long attracted keen attention in the research and policy arena. While there may be various motivations to study health inequality, what distinguishes it as a topic is moral concern. Despite the importance of this moral interest, a theoretical and analytical framework for measuring health inequality acknowledging moral concerns remains to be established. STUDY OBJECTIVE: To propose a framework for measuring the moral or ethical dimension of health inequality-that is, health inequity. DESIGN: Conceptual discussion. CONCLUSIONS: Measuring health inequity entails three steps: (1) defining when a health distribution becomes inequitable, (2) deciding on measurement strategies to operationalize a chosen concept of equity, and (3) quantifying health inequity information. For step (1) a variety of perspectives on health equity exist under two categories, health equity as equality in health, and health inequality as an indicator of general injustice in society. In step (2), when we are interested in health inequity, the choice of the measurement of health, the unit of time, and the unit of analysis in health inequity analysis should reflect moral considerations. In step (3) we must follow principles rather than convenience and consider six questions that arise when quantifying health inequity information. This proposed framework suggests various ways to conceptualize the moral dimension of health inequality and emphasises the logical consistency from conception to measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".