Assessing financial protection in health: Does the choice of poverty line matter?
Bibliographic record
Abstract
Financial protection in health is an essential aspect of the universal health coverage discourse. It is about ensuring that paying for health services does not affect the ability of households and individuals to afford necessities. A well-known way to assess financial protection is whether or not people are pushed into-or further into-poverty by paying out-of-pocket for health services. Although impoverishment from out-of-pocket health spending is not an explicit indicator of the sustainable development goals, it has gained prominence among researchers and policymakers because of its intuitive appeal and link to overall poverty reduction. Using data from Nigeria, this paper demonstrates that the choice of poverty line matters for assessing the impoverishing effect of paying out-of-pocket for health services. Among other things, the inconsistencies (or lack of dominance) could occur in ranking impoverishment levels by mutually exclusive groups within a country or in ranking different countries or a country over time. The implication is that the choice of poverty line could lead to manipulation of results for policy and for supporting an agenda that demonstrates an improvement in financial protection when this may not necessarily be the case.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".