Improving maternal and child health policymaking processes in Nigeria: an assessment of policymakers’ needs, barriers and facilitators of evidence-informed policymaking
Bibliographic record
Abstract
BACKGROUND: In Nigeria, interest in the evidence-to-policy process is gaining momentum among policymakers involved in maternal, newborn and child health (MNCH). However, numerous gaps exist among policymakers on use of research evidence in policymaking. The objective of this study was to assess the perception of MNCH policymakers regarding their needs and the barriers and facilitators to use of research evidence in policymaking in Nigeria. METHODS: The study design was a cross-sectional assessment of perceptions undertaken during a national MNCH stakeholders' engagement event convened in Abuja, Nigeria. A questionnaire designed to assess participants' perceptions was administered in person. Group consultations were also held, which centred on policymakers' evidence-to-policy needs to enhance the use of evidence in policymaking. RESULTS: A total of 40 participants completed the questionnaire and participated in the group consultations. According to the respondents, the main barriers to evidence use in MNCH policymaking include inadequate capacity of organisations to conduct policy-relevant research; inadequate budgetary allocation for policy-relevant research; policymakers' indifference to research evidence; poor dissemination of research evidence to policymakers; and lack of interaction fora between researchers and policymakers. The main facilitators of use of research evidence for policymaking in MNCH, as perceived by the respondents, include capacity building for policymakers on use of research evidence in policy formulation; appropriate dissemination of research findings to relevant stakeholders; involving policymakers in research design and execution; and allowing policymakers' needs to drive research. The main ways identified to promote policymakers' use of evidence for policymaking included improving policymakers' skills in information and communication technology, data use, analysis, communication and advocacy. CONCLUSION: To improve the use of research evidence in policymaking in Nigeria, there is a need to establish mechanisms that will facilitate the movement from evidence to policy and address the needs identified by policymakers. It is also imperative to improve organisational initiatives that facilitate use of research evidence for policymaking.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".