On the Measurement of Financial Protection: An Assessment of the Usefulness of the Catastrophic Health Expenditure Indicator to Monitor Progress Towards Universal Health Coverage
Bibliographic record
Abstract
Ensuring financial protection (FP) against health expenditures is a key component of Sustainable Development Goal (SDG) 3.8, which aims to achieve Universal Health Coverage (UHC). While the proportion of households with catastrophic health expenditures exceeding a proportion of their total income or consumption has been adopted as the official SDG indicator, other approaches exist and it is unclear how useful the official indicator is in tracking progress toward the FP sub-target across countries and across time. This paper evaluates the usefulness of the official SDG indicator to measure FP using the RACER framework and discusses how alternative indicators may improve upon the limitations of the official SDG indicator for global monitoring purposes. We find that while all FP indicators have some disadvantages, the official SDG indicator has some properties that severely limit its usefulness for global monitoring purposes. We recommend more research to understand how alternative indicators may enhance global monitoring, as well as improvements to the quality and quantity of underlying data to construct FP indicators in order to improve efforts to monitor progress toward 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 | 0.065 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".