Fostering global primary care research: a capacity-building approach
Bibliographic record
Abstract
The Alma Ata and Astana Declarations reaffirm the importance of high-quality primary healthcare (PHC), yet the capacity to undertake PHC research—a core element of high-quality PHC—in low-income and middle-income countries (LMIC) is limited. Our aim is to explore the current risks or barriers to primary care research capacity building, identify the ongoing tensions that need to be resolved and offer some solutions, focusing on emerging contexts. This paper arose from a workshop held at the 2019 North American Primary Care Research Group Annual Meeting addressing research capacity building in LMICs. Five case studies (three from Africa, one from South-East Asia and one from South America) illustrate tensions and solutions to strengthening PHC research around the world. Research must be conducted in local contexts and be responsive to the needs of patients, populations and practitioners in the community. The case studies exemplify that research capacity can be strengthened at the micro (practice), meso (institutional) and macro (national policy and international collaboration) levels. Clinicians may lack coverage to enable research time; however, practice-based research is precisely the most relevant for PHC. Increasing research capacity requires local skills, training, investment in infrastructure, and support of local academics and PHC service providers to select, host and manage locally needed research, as well as to disseminate findings to impact local practice and policy. Reliance on funding from high-income countries may limit projects of higher priority in LMIC, and ‘brain drain’ may reduce available research support; however, we provide recommendations on how to deal with these tensions.
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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 | 0.216 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.015 | 0.061 |
| Scholarly communication | 0.033 | 0.031 |
| Open science | 0.010 | 0.080 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".