Equity of financial protection for health in high-income countries: scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Financial protection (FP) is a central function of health systems to enhance access to essential care and improve health equity. We aim to characterise evidence on the distribution of FP in high-income countries as well as how equity of FP is conceptualised and measured in these settings. Findings from this review can advance methodological and conceptual knowledge about equity in FP, guide the evaluation of health systems and inform policy on eliminating inequitable barriers to care to achieve universal health coverage. METHODS AND ANALYSIS: and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. We will search four academic databases covering health sciences and economic literature as well as four grey literature sources for relevant publications. Screening for eligibility will be performed independently by two reviewers after calibration of screening criteria. Data will be charted using a standardised form and summarised by thematic analysis. ETHICS AND DISSEMINATION: Institutional research ethics review was not required; however, research ethics will be considered iteratively throughout the research process. Research findings will be disseminated to scientific and policy meetings, summarised for lay audiences and submitted for publication in a peer-reviewed journal.
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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 | 0.124 | 0.114 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.017 |
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".