[Equity-oriented monitoring in the context of universal health coverage].
Bibliographic record
Abstract
Monitoring inequalities in health is fundamental to the equitable and progressive realization of universal health coverage (UHC). A successful approach to global inequality monitoring must be intuitive enough for widespread adoption, yet maintain technical credibility. This article discusses methodological considerations for equity-oriented monitoring of UHC, and proposes recommendations for monitoring and target setting. Inequality is multidimensional, such that the extent of inequality may vary considerably across different dimensions such as economic status, education, sex, and urban/rural residence. Hence, global monitoring should include complementary dimensions of inequality (such as economic status and urban/rural residence) as well as sex. For a given dimension of inequality, subgroups for monitoring must be formulated taking into consideration applicability of the criteria across countries and subgroup heterogeneity. For economic-related inequality, we recommend forming subgroups as quintiles, and for urban/rural inequality we recommend a binary categorization. Inequality spans populations, thus appropriate approaches to monitoring should be based on comparisons between two subgroups (gap approach) or across multiple subgroups (whole spectrum approach). When measuring inequality absolute and relative measures should be reported together, along with disaggregated data; inequality should be reported alongside the national average. We recommend targets based on proportional reductions in absolute inequality across populations. Building capacity for health inequality monitoring is timely, relevant, and important. The development of high-quality health information systems, including data collection, analysis, interpretation, and reporting practices that are linked to review and evaluation cycles across health systems, will enable effective global and national health inequality monitoring. These actions will support equity-oriented progressive realization of UHC.
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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 | 0.053 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".