Primary care financing: a systematic assessment of research priorities in low- and middle-income countries
Bibliographic record
Abstract
INTRODUCTION: Financing of primary healthcare (PHC) is the key to the provision of equitable universal care. We aimed to identify and prioritise the perceived needs of PHC practitioners and researchers for new research in low- and middle-income countries (LMIC) about financing of PHC. METHODS: Three-round expert panel consultation using web-based surveys of LMIC PHC practitioners, academics and policy-makers sampled from global networks. Iterative literature review conducted in parallel. First round (Pre-Delphi survey) elicited possible research questions to address knowledge gaps about financing. Responses were independently coded, collapsed and synthesised to two lists of questions. Round 2 (Delphi Round 1) invited panellists to rate importance of each question. In Round 3 (Delphi Round 2), panellists ranked questions in order of importance. RESULTS: A diverse range of PHC practitioners, academics and policy-makers in LMIC representing all global regions identified 479 knowledge gaps as potentially critical to improving PHC financing. Round 2 provided 31 synthesised questions on financing for rating. The top 16 were ranked in Round 3e to produce four prioritised research questions. CONCLUSIONS: This novel exercise created an expansive and prioritised list of critical knowledge gaps in PHC financing research questions. This offers valuable guidance to global supporters of primary care evaluation and implementation, including research funders and academics seeking research priorities. The source and context specificity of this research, informed by LMIC practitioners and academics on a global and local basis, should increase the likelihood of local relevance and eventual success in implementing the findings.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".