Primary Care Research Priorities in Low-and Middle-Income Countries
Bibliographic record
Abstract
PURPOSE: To identify and prioritize the needs for new research evidence for primary health care (PHC) in low-and middle-income countries (LMICs) about organization, models of care, and financing of PHC. METHODS: Three-round expert panel consultation of LMIC PHC practitioners and academics sampled from global networks, via web-based surveys. Iterative literature review conducted in parallel. Round 1 (pre-Delphi survey) elicited possible research questions to address knowledge gaps about organization and models of care and about financing. Round 2 invited panelists to rate the importance of each question, and in round 3 panelists provided priority ranking. RESULTS: One hundred forty-one practitioners and academics from 50 LMICs from all global regions participated and identified 744 knowledge gaps critical to improving PHC organization and 479 for financing. Four priority areas emerged: effective transition of primary and secondary services, horizontal integration within a multidisciplinary team and intersectoral referral, integration of private and public sectors, and ways to support successfully functioning PHC professionals. Financial evidence priorities were mechanisms to drive investment into PHC, redress inequities, increase service quality, and determine the minimum necessary budget for good PHC. CONCLUSIONS: This novel approach toward PHC needs in LMICs, informed by local academics and professionals, created an expansive and prioritized list of critical knowledge gaps in PHC organization and financing. It resulted in research questions, offering valuable guidance to global supporters of primary care evaluation and implementation. Its source and context specificity, informed by LMIC practitioners and academics, should increase the likelihood of local relevance and eventual success in implementing research findings.
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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 | 0.083 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".