Lessons learned from descriptions and evaluations of knowledge translation platforms supporting evidence-informed policy-making in low- and middle-income countries: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) platforms are organisations, initiatives and networks that focus on supporting evidence-informed policy-making at least in part about the health-system arrangements that determine whether the right programmes, services and products get to those who need them. Many descriptions and evaluations of KT platforms in low- and middle-income countries have been produced but, to date, they have not been systematically reviewed. METHODS: We identified potentially relevant studies through a search of five electronic databases and a variety of approaches to identify grey literature. We used four criteria to select eligible empirical studies. We extracted data about seven characteristics of included studies and about key findings. We used explicit criteria to assess study quality. In synthesising the findings, we gave greater attention to themes that emerged from multiple studies, higher-quality studies and different contexts. RESULTS: Country was the most common jurisdictional focus of KT platforms, EVIPNet the most common name and high turnover among staff a common infrastructural feature. Evidence briefs and deliberative dialogues were the activities/outputs that were the most extensively studied and viewed as helpful, while rapid evidence services were the next most studied but only in a single jurisdiction. None of the summative evaluations used a pre-post design or a control group and, with the exception of the evaluations of the influence of briefs and dialogues on intentions to act, none of the evaluations achieved a high quality score. CONCLUSIONS: A large and growing volume of research evidence suggests that KT platforms offer promise in supporting evidence-informed policy-making in low- and middle-income countries. KT platforms should consider as next steps expanding their current, relatively limited portfolio of activities and outputs, building bridges to complementary groups, and planning for evaluations that examine 'what works' for 'what types of issues' in 'what types of contexts'.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it