A global perspective of the role of the maternal and child health handbook in health promotion: Narrative synthesis
Bibliographic record
Abstract
Little is known about the impact of "home-based records'' on the health promotion of mothers and children. Considering this, we compiled and analysed existing evidence on the effectiveness of a specific home-based record, the Maternal and Child Health Handbook (MCHHB), in enhancing the health of mothers and their children. A systematic search of PubMed, Google Scholar, Maternity, and Infant Care, CINHAL, and Ovid was conducted. All types of original research articles published in English were considered. A narrative synthesis was used due to the heterogeneity of findings among the included studies. Out of a total of 1351 papers, 45 studies were included. Breastfeeding, immunisation, family planning, antenatal care, maternal nutrition, maternal Tetanus Toxoid (TT) immunisation, vitamin A and iron supplements, smoking and alcohol consumption during pregnancy, healthy and safe delivery, awareness of pregnancy complications, and healthy child development are all areas where MCHHB has been implemented and evaluated. Although one study found no effect, our findings indicate a positive impact. The results emphasised the effectiveness and value of MCHHB in enhancing maternal and infant health. However, given that only a small number of studies were available for each outcome group, we suggest more research be conducted on the MCHHB's positive effects on mothers’ and children’s health.
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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 | 0.034 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".