Measuring health inequalities in the context of sustainable development goals
Bibliographic record
Abstract
Transforming our world: the 2030 agenda for sustainable development promotes the improvement of health equity, which entails ongoing monitoring of health inequalities. The World Health Organization has developed a multistep approach to health inequality monitoring consisting of: (i) determining the scope of monitoring; (ii) obtaining data; (iii) analysing data; (iv) reporting results; and (v) implementing changes. Technical considerations at each step have implications for the results and conclusions of monitoring and subsequent remedial actions. This paper presents some technical considerations for developing or strengthening health inequality monitoring, with the aim of encouraging more robust, systematic and transparent practices. We discuss key aspects of measuring health inequalities that are relevant to steps (i) and (iii). We highlight considerations related to the selection, measurement and categorization of dimensions of health inequality, as well as disaggregation of health data and calculation of summary measures of inequality. Inequality monitoring is linked to health and non-health aspects of the 2030 agenda for sustainable development, and strong health inequality monitoring practices can help to inform equity-oriented policy directives.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".