Implementation research evidence uptake and use for policy-making
Bibliographic record
Abstract
A major obstacle to the progress of the Millennium Development Goals has been the inability of health systems in many low- and middle-income countries to effectively implement evidence-informed interventions. This article discusses the relationships between implementation research and knowledge translation and identifies the role of implementation research in the design and execution of evidence-informed policy. After a discussion of the benefits and synergies needed to translate implementation research into action, the article discusses how implementation research can be used along the entire continuum of the use of evidence to inform policy. It provides specific examples of the use of implementation research in national level programmes by looking at the scale up of zinc for the treatment of childhood diarrhoea in Bangladesh and the scaling up of malaria treatment in Burkina Faso. A number of tested strategies to support the transfer of implementation research results into policy-making are provided to help meet the standards that are increasingly expected from evidence-informed policy-making practices.
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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 | 0.532 | 0.733 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".