Promoting evidence informed policy making in Nigeria: a review of the maternal, newborn and child health policy development process
Bibliographic record
Abstract
Background: There is increasing recognition worldwide that health policymaking process should be informed by best available evidence. The purpose of this study was to review the policy documents on maternal, newborn and child health (MNCH) in Nigeria to assess the extent evidence informed policymaking mechanism was employed in the policy formulation process. Methods: A comprehensive literature search of websites of the Federal Ministry of Health(FMOH) Nigeria and other related ministries and agencies for relevant health policy documents related to MNCH from year 2000 to 2015 was undertaken. The following terms were used interchangeably for the literature search: maternal, child, newborn, health, policy, strategy,framework, guidelines, Nigeria. Results: Of the 108 policy documents found, 19 (17.6%) of them fulfilled the study inclusion criteria. The policy documents focused on the major aspects of maternal health improvements in Nigeria such as reproductive health, anti-malaria treatment, development of adolescent and young people health, mid wives service scheme, prevention of mother to child transmission of HIV and family planning. All the policy documents indicated that a consultative process of collection of input involving multiple stakeholders was employed, but there was no rigorous scientific process of assessing, adapting, synthesizing and application of scientific evidence reported in the policy development process. Conclusion: It is recommended that future health policy development process on MNCH should follow evidence informed policy making process and clearly document the process of incorporating evidence in the policy development.
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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 | 0.095 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| 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".