Male partners' participation in birth preparedness and complication readiness in global south: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: The lower priority given for involving male partners in birth preparedness and complication readiness contributes to the poor maternal and neonatal health outcomes. Male partners in low- and middle-income countries determine women’s access to and affordability of health services and directly influence their health outcomes. This systematic review and meta-analysis determine the pooled magnitude of the male partner’s participation in birth preparedness and complication readiness in the global south.Methods: Literature published in the English language from 2004 to 2019 was retrieved using appropriate search terms on Google Scholar, PubMed/MEDLINE, CINHAL, Scopus, and Embase. The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) was used for critical appraisal of studies. A pooled statistical meta-analysis was conducted using STATA Version 14.0 software. The heterogeneity and publication bias were assessed using the I2 statistics and Egger’s test, respectively. Duval and Tweedie's nonparametric trim and fill analysis using the random-effect analysis. The Random effects model was used to estimate the summary prevalence of BPCR and the corresponding 95% confidence intervals (CI). Results: 12 studies and 4210 participants were included. The overall pooled result showed 52.3% of male partners of pregnant women and nursing mothers were prepared for birth and its complications. Forty-nine percent of male partners saved money for delivery and 40.5% of them identified skilled birth attendants, and 55% of male partners saved money for birth or its complication. Only 42.1% of male partners arranged transportation and 59.8% of them identified the place of birth. Only 18.9% of male partners identified a potential blood donor for emergency cases of childbirth and postpartum complication.Conclusions: A low proportion of male partners of pregnant women and nursing mothers were prepared for childbirth and its complications. Countries in the global south region should review their health care policies and design strategies to improve the birth preparedness and complication readiness practice among male partners of pregnant women and nursing mothers.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".