Promoting evidence informed policymaking for maternal and child health in Nigeria: lessons from a knowledge translation workshop
Why this work is in the frame
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Bibliographic record
Abstract
Background: Knowledge translation (KT) is a process that ensures that research evidence gets translated into policy and practice. In Nigeria, reports indicate that research evidence rarely gets into policy making process. A major factor responsible for this is lack of KT capacity enhancement mechanisms. The objective of this study was to improve KT competence of an implementation research team (IRT), policymakers and stakeholders in maternal and child health to enhance evidence-informed policy making. Methods: This study employed a "before and after" design, modified as an intervention study. The study was conducted in Bauchi, north-eastern Nigeria. A three-day KT training workshop was organized and 15 modules were covered including integrated and end-of-grant KT; KT models,measures, tools and strategies; priority setting; managing political interference; advocacy and consensus building/negotiations; inter-sectoral collaboration; policy analysis, contextualization and legislation. A 4-point Likert scale pre-/post-workshop questionnaires were administered to evaluate the impact of the training, it was designed in terms of extent of adequacy; with "grossly inadequate" representing 1 point, and "very adequate" representing 4 points.Results: A total of 45 participants attended the workshop. There was a noteworthy improvement in the participants’ understanding of KT processes and strategies. The range of the praiseworthiness of participants knowledge of modules taught was from 2.04-2.94, the range for the post workshop mean was from 3.10–3.70 on the 4-point Likert scale. The range of percentage increase in mean for participants’ knowledge at the end of the workshop was from 13.3%–55.2%.Conclusion: The outcome of this study suggests that using a KT capacity building programme e.g., workshop, health researchers, policymakers and other stakeholders can acquire capacity and skill that will facilitate evidence-to-policy link.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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