Community perspectives on maternal dietary diversity in rural Kenya, Mozambique and The Gambia: A PRECISE Network qualitative study
Bibliographic record
Abstract
Pregnant and lactating women in sub-Saharan Africa are vulnerable to micronutrient inadequacies, with risk of adverse pregnancy outcomes. Adequate intakes of diverse foods are associated with better micronutrient status and recommended by the World Health Organization as part of healthy eating counselling during antenatal care. However, our understanding of community knowledge of dietary diversity within the context of maternal diets is limited. We used a descriptive qualitative approach to explore community perceptions of dietary diversity during pregnancy and lactation, as well as influencing factors in sub-Saharan Africa. A total of 47 in-depth interviews were conducted between May and October 2022 in Kenya, Mozambique and The Gambia with a purposively drawn sample of pregnant women and mothers who had delivered within two years preceding the data collection, their male and female relatives, and community opinion leaders. Other methods included participant observation and photovoice. Data were analyzed using a thematic approach on NVivo software. Dietary diversity was found to be well aligned with local perceptions of healthy meals. All participants were able to differentiate between starchy staple grains and additional foods to provide nutrients. While diverse meals were valued for pregnant and lactating mothers, participants across the three countries shared that maternal diets were not more diverse compared to typical household meals. Furthermore, diverse diets were inaccessible for many in their communities, due to challenges in affordability, seasonality, gender norms, knowledge and preferences. Adequate nutrition knowledge, accessibility of foods, and support of household decision-makers, particularly husbands and partners, were all identified as critical to ensure women have adequate diverse maternal diets.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".