An assessment of maternal, newborn and child health implementation studies in Nigeria: implications for evidence informed policymaking and practice
Bibliographic record
Abstract
BACKGROUND: The introduction of implementation science into maternal, newborn and child health (MNCH) research has facilitated better methods to improve uptake of research findings into practices. With increase in implementation research related to MNCH world-wide, stronger scientific evidence are now available and have improved MNCH policies in many countries including Nigeria. The purpose of this study was to review MNCH implementation studies undertaken in Nigeria in order to understand the extent the evidence generated informed better policy. METHODS: This study was a systematic review. A MEDLINE Entrez PubMed search was performed in August 2015 and implementation studies that investigated MNCH in Nigeria from 1966 to 2015 in relation to health policy were sought. Search key words included Nigeria, health policy, maternal, newborn, and child health. Only policy relevant studies that were implementation or intervention research which generated evidence to improve MNCH in Nigeria were eligible and were selected. RESULTS: A total of 18 relevant studies that fulfilled the study inclusion criteria were identified out of 471 studies found. These studies generated high quality policy relevance evidence relating to task shifting, breastfeeding practices, maternal nutrition, childhood immunization, kangaroo mother care (KMC), prevention of maternal to child transmission of HIV, etc. These indicated significant improvements in maternal health outcomes in localities and health facilities where the studies were undertaken. CONCLUSION: There is a dire need for more implementation research related to MNCH in low income settings because the priority for improved MNCH outcome is not so much the development of new technologies but solving implementation issues, such as how to scale up and evaluate interventions within complex health systems.
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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 | 0.301 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.040 | 0.038 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".