Correlates of maternal, newborn and child health services uptake, including male partner involvement: Baseline survey results from Bangladesh
Bibliographic record
Abstract
South Asia bears a substantial proportion of the global maternal mortality burden, with adolescents disproportionately affected. Bangladesh has one of the highest adolescent pregnancy rates in the world, with low utilisation of maternal newborn and child health (MNCH) services. This hampers the country’s efforts to achieve optimal health outcomes as envisioned by the Sustainable Development Goals. Male partner involvement is a recognised approach to optimise access to services and decision-making. In South Asia data on male involvement in MNCH service uptake is limited. Plan International’s Strengthening Health Outcomes for Women and Children was implemented across four districts in Bangladesh between 2016 and 2020 and aimed to address these issues. Study results (N = 1,724) found higher maternal education levels were associated with use of MNCH services. After controlling for maternal education, service uptake was associated with male partner support level and perceived joint decision-making. The positive association between male support level and MNCH scale was robust to stratification by maternal education level, and by age group (i.e. adolescent vs. adult mothers). These findings suggest that one path for achieving optimal MNCH outcomes might be through structural-level interventions centred on women, combined with components targeting male partners or male heads of households.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".