Engagement of Husbands in a Maternal Nutrition Program Substantially Contributed to Greater Intake of Micronutrient Supplements and Dietary Diversity during Pregnancy: Results of a Cluster-Randomized Program Evaluation in Bangladesh
Bibliographic record
Abstract
Background: Although husbands may provide support during pregnancy, limited evidence exists on how to promote husbands' engagement and what impact it has. Alive & Thrive integrated nutrition-focused interventions, targeting both wives and husbands, through an existing Maternal, Neonatal, and Child Health (MNCH) platform in Bangladesh. Objectives: We evaluated 1) the impact of a nutrition-focused MNCH program, compared with the standard MNCH program, on husbands' behavioral determinants (i.e., awareness, knowledge, self-efficacy) and support to wives to adopt optimal nutrition practices and 2) how much of the previously documented impact on women's supplement intake and dietary diversity was explained by husbands' behavioral determinants and support. Methods: We used a cluster-randomized design with cross-sectional surveys at baseline (2015) and endline (2016) (n = ∼1000 women and ∼700 husbands/survey). We used mixed linear regression accounting for clustering to estimate difference-in-differences (DIDs) for impact on husbands' behavioral determinants and path analysis to examine how much these determinants explained the impact on women's nutrition behaviors. Results: Of husbands in the nutrition-focused MNCH group, 62% were counseled by health workers, 66% attended a husbands' forum, and 34% saw video shows. The nutrition-focused MNCH, compared with the standard MNCH group, resulted in greater husbands' awareness (DID: 2.74 of 10 points), knowledge (DID: 1.31), self-efficacy and social norms with regard to optimal nutrition practices (difference: 1.08), and support to their wives (DID: 1.86). Husbands' behavioral determinants and support explained nearly half of the program impact for maternal supplement intake and one-quarter for dietary diversity. Conclusions: A nutrition-focused MNCH program that promoted and facilitated husbands' engagement during their wives' pregnancies significantly improved husbands' awareness, knowledge, self-efficacy, and support. These improvements substantially explained the program's impact on women's intake of micronutrient supplements and dietary diversity. Targeting wives and husbands and designing activities to engage men in maternal nutrition programs are important to maximize impact. This trial was registered at www.clinicaltrials.gov as NCT02745249.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".