What research tells us about knowledge transfer strategies to improve public health in low-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVES: This study describes the current state of research on knowledge transfer strategies to improve public health in low-income countries, to identify the knowledge gaps on this topic. METHODS: In this scoping review, a descriptive and systematic process was used to analyse, for each article retained, descriptions of research context and methods, types of knowledge transfer activities and results reported. RESULTS: 28 articles were analysed. They dealt with the evaluation of transfer strategies that employed multiple activities, mostly targeting health professionals and women with very young children. Most often these studies used quantitative designs and measurements of instrumental use with some methodological shortcomings. Results were positive and suggested recommendations for improving professional practices, knowledge and health-related behaviours. The review highlights the great diversity of transfer strategies used, strategies and many conditions for knowledge use. CONCLUSIONS: The review provides specific elements for understanding the transfer processes in low-income countries and highlights the need for systematic evaluation of the conditions for research results utilization.
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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 | 0.028 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".