Promoting evidence informed policymaking for maternal and child health in Nigeria: lessons from a knowledge translation workshop
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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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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".