Centering community-based maternal and child nutrition services in Bangladesh’s rural primary healthcare: what has potential to scale
Bibliographic record
Abstract
Introduction: The extensive network of community health workers in rural Bangladesh has the potential to deliver maternal and child nutrition services, while promoting linkages with healthcare facilities. A strategy for strengthening community-based nutrition services was developed and tested. Methods: The three-phased strategy included review of existing community-based systems, co-designing service package with multi-sector government representatives, and testing implementation feasibility. Integrated health and nutrition service delivery, supportive supervision, and increased accountability of local government were core components of the service package being implemented in selected geographies since March 2023. The assessment followed a mixed-method design with household survey of 1,166 pregnant women, mothers of children under-6 months and 6-23 months, and observations of 965 service delivery points along with qualitative study. Results: A higher proportion of children received growth monitoring and promotion (GMP) services through expanded program on immunization (EPI) sessions, with better compliance to service delivery protocol in intervention areas compared with controls. Maternal nutrition services of gestational weight gain monitoring and distribution of supplements were better available in intervention areas. However, minimum dietary diversity among pregnant women (69% intervention, 72% control), early initiation of breastfeeding (55% intervention, 51% control), and complementary feeding practices were comparable in intervention and control areas. Nutrition services were successfully integrated in supervision which earlier covered EPI and family planning. The local government contributed to strengthening nutrition services but at a small scale. Discussion: There is potential to scale-up GMP services through EPI and merging antenatal clinics with GMP and EPI such that all maternal and childcare services are available at the same place and same time. A coordinated investment and oversight from multiple national government departments is needed. At district and sub-district levels, scale-up requires joint annual planning of nutrition and EPI services, strengthened management of nutrition services, bridging health worker vacancies, introducing volunteers in sites with high EPI case load, capacity building, and supportive supervision. Replacement of multiple health and nutrition records with a single mother and child health and nutrition card is also feasible. However, behavior change interventions through home visits and courtyard meetings need more testing before recommending scale-up.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".