Closing the gap between implementation science and policy in Nigeria: lessons from the Nigeria implementation science alliance using a nominal group technique
Bibliographic record
Abstract
Introduction: Knowledge translation in healthcare has been of keen interest to researchers, practitioners, policymakers and administrators as it seeks to confront complex health issues within communities by closing the gap between knowledge generation through research and knowledge application. A paucity of information exists regarding nature of the relationship between Nigerian implementation science researchers and policymakers in the sphere of knowledge translation. This study aimed to identify and discuss barriers to successful engagement between implementation researchers and policymakers as well as to identify strategies for successful engagement between both parties in Nigeria. Methods: A modified Nominal Group Technique was conducted with 259 diverse health research stakeholders attending the 7th Nigeria Implementation Science Alliance conference in Abuja, Nigeria, to identify barriers to knowledge translation in Nigerian healthcare settings. Results: Lack of interest in non-aligned priorities of implementation researchers and policymakers, knowledge and capacity gap in stakeholder engagement, and non-existence of engagement framework were ranked as the top three barriers. Developing and sustaining an effective engagement framework, aligning researcher-policymaker interests through collaborative research projects, and joint capacity-building were ranked the topmost facilitators of researcher-policymaker engagement. Conclusion: This study highlights key barriers to research-to-policy engagement in Nigeria, namely the need for structured engagement frameworks, alignment of priorities, and targeted capacity development, and proposes actionable strategies to address them. Sustainable impact will depend on dedicated financing, governance reforms, and institutional changes, supported by long-term partnerships and robust evaluation systems to advance knowledge translation and improve health outcomes.
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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 | 0.146 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".