Research gaps in the organisation of primary healthcare in low-income and middle-income countries and ways to address them: a mixed-methods approach
Bibliographic record
Abstract
INTRODUCTION: Since the Alma-Ata Declaration 40 years ago, primary healthcare (PHC) has made great advances, but there is insufficient research on models of care and outcomes-particularly for low-income and middle-income countries (LMICs). Systematic efforts to identify these gaps and develop evidence-based strategies for improvement in LMICs has been lacking. We report on a global effort to identify and prioritise the knowledge needs of PHC practitioners and researchers in LMICs about PHC organisation. METHODS: Three-round modified Delphi using web-based surveys. PHC practitioners and academics and policy-makers from LMICs sampled from global networks. First round (pre-Delphi survey) collated possible research questions to address knowledge gaps about organisation. Responses were independently coded, collapsed and synthesised. Round 2 (Delphi round 1) invited panellists to rate importance of each question. In round 3 (Delphi round 2), panellists ranked questions into final order of importance. Literature review conducted on 36 questions and gap map generated. RESULTS: Diverse range of practitioners and academics in LMICs from all global regions generated 744 questions for PHC organisation. In round 2, 36 synthesised questions on organisation were rated. In round 3, the top 16 questions were ranked to yield four prioritised questions in each area. Literature reviews confirmed gap in evidence on prioritised questions in LMICs. CONCLUSION: In line with the 2018 Astana Declaration, this mixed-methods study has produced a unique list of essential gaps in our knowledge of how best to organise PHC, priority-ordered by LMIC expert informants capable of shaping their mitigation. Research teams in LMIC have developed implementation plans to answer the top four ranked research questions.
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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 | 0.261 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".