Mitigating the regressivity of private mechanisms of financing healthcare: An Assessment of 29 countries
Bibliographic record
Abstract
Progressive financing of health care can help advance the equity and financial protection goals of health systems. All countries' health systems are financed in part through private mechanisms, including out-of-pocket payments and voluntary health insurance. Yet little is known about how these financing schemes are structured, and the extent to which policies in place mitigate regressivity. This study identifies the potential policies to mitigate regressivity in private financing, builds two qualitative tools to comparatively assess regressivity of these two sources of revenue, and applies this tool to a selection of 29 high-income countries. It provides new evidence on the variations in policy approaches taken, and resultant regressivity, of private mechanisms of financing health care. These results inform a comprehensive assessment of progressivity of health systems financing, considering all revenue streams, that appears in this special section of the journal.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".