Identifying priorities for research on financial risk protection to achieve universal health coverage: a scoping overview of reviews
Bibliographic record
Abstract
OBJECTIVES: Financial risk protection (FRP) is an indicator of the Sustainable Development Goal 3 universal health coverage (UHC) target. We sought to characterise what is known about FRP in the UHC context and to identify evidence gaps to prioritise in future research. DESIGN: Scoping overview of reviews using the Arksey & O'Malley and Levac & Colquhoun framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews reporting guidelines. DATA SOURCES: MEDLINE, PsycINFO, CINAHL-Plus and PAIS Index were systematically searched for studies published between 1 January 1995 and 20 July 2021. ELIGIBILITY CRITERIA: Records were screened by two independent reviewers in duplicate using the following criteria: (1) literature review; (2) focus on UHC achievement through FRP; (3) English or French language; (4) published after 1995 and (5) peer-reviewed. DATA EXTRACTION AND SYNTHESIS: Two reviewers extracted data using a standard form and descriptive content analysis was performed to synthesise findings. RESULTS: 50 studies were included. Most studies were systematic reviews focusing on low-income and middle-income countries. Study periods spanned 1990 and 2020. While FRP was recognised as a dimension of UHC, it was rarely defined as a concept. Out-of-pocket, catastrophic and impoverishing health expenditures were most commonly used to measure FRP. Pooling arrangements, expansion of insurance coverage and financial incentives were the main interventions for achieving FRP. Evidence gaps pertained to the effectiveness, cost-effectiveness and equity implications of efforts aimed at increasing FRP. Methodological gaps related to trade-offs between single-country and multicountry analyses; lack of process evaluations; inadequate mixed-methods evidence, disaggregated by relevant characteristics; lack of comparable and standardised measurement and short follow-up periods. CONCLUSIONS: This scoping overview of reviews characterised what is known about FRP as a UHC dimension and found evidence gaps related to the effectiveness, cost-effectiveness and equity implications of FRP interventions. Theory-informed mixed-methods research using high-quality, longitudinal and disaggregated data is needed to address these objectives.
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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 | 0.113 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.041 | 0.034 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".