Engaging stakeholders to identify gaps and develop strategies to inform evidence use for health policymaking in Nigeria
Bibliographic record
Abstract
Introduction: recent efforts to bridge the evidence-policy gap in low-and middle-income countries have seen growing interest from key audiences such as government, civil society, international organizations, private sector players, academia, and media. One of such engagement was a two-day virtual participant-driven conference (the convening) in Nigeria. The aim of the convening was to develop strategies for improving evidence use in health policy. The convening witnessed a participant blend of health policymakers, researchers, political policymakers, philanthropists, global health practitioners, program officers, students, and the media. Methods: in this study, we analyzed conversations at the convening with the aim to disseminate findings to key stakeholders in Nigeria. The recordings from the convening were transcribed and analyzed inductively to identify emerging themes, which were interpreted, and inferences are drawn. Results: a total of 630 people attended the convening. Participants joined from 13 countries. Participants identified poor collaboration between researchers and policymakers, poor community involvement in research and policy processes, poor funding for research, and inequalities as key factors inhibiting the use of evidence for policymaking in Nigeria. Strategies proposed to address these challenges include the use of participatory and embedded research methods, leveraging existing systems and networks, advocating for improved funding and ownership for research, and the use of context-sensitive knowledge translation strategies. Conclusion: overall, better interaction among the various stakeholders will improve the evidence generation, translation, and use in Nigeria. A road map for the dissemination of findings from this conference has been developed for implementation across the strata of the health system.
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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 | 0.094 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".