Bridging the gaps between research, policy and practice in low- and middle-income countries: a survey of researchers
Bibliographic record
Abstract
BACKGROUND: Many international statements have urged researchers, policy-makers and health care providers to collaborate in efforts to bridge the gaps between research, policy and practice in low- and middle-income countries. We surveyed researchers in 10 countries about their involvement in such efforts. METHODS: We surveyed 308 researchers who conducted research on one of four clinical areas relevant to the Millennium Development Goals (prevention of malaria, care of women seeking contraception, care of children with diarrhea and care of patients with tuberculosis) in each of 10 low- and middle-income countries (China, Ghana, India, Iran, Kazakhstan, Laos, Mexico, Pakistan, Senegal and Tanzania). We focused on their engagement in three promising bridging activities and examined system-level, organizational and individual correlates of these activities. RESULTS: Less than half of the researchers surveyed reported that they engaged in one or more of the three promising bridging activities: 27% provided systematic reviews of the research literature to their target audiences, 40% provided access to a searchable database of research products on their topic, and 43% established or maintained long-term partnerships related to their topic with representatives of the target audience. Three factors emerged as statistically significant predictors of respondents' engagement in these activities: the existence of structures and processes to link researchers and their target audiences predicted both the provision of access to a database (odds ratio [OR] 2.62, 95% CI 1.30-5.27) and the establishment or maintenance of partnerships (OR 2.65, 95% CI 1.25-5.64); stability in their contacts predicted the provision of systematic reviews (OR 2.88, 95% CI 1.35-6.13); and having managers and public (government) policy-makers among their target audiences predicted the provision of both systematic reviews (OR 4.57, 95% CI 1.78-11.72) and access to a database (OR 2.55, 95% CI 1.20-5.43). INTERPRETATION: Our findings suggest potential areas for improvement in light of the bridging strategies targeted at health care providers that have been found to be effective in some contexts and the factors that appear to increase the prospects for using research in policy-making.
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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 | 0.045 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".