Nutritional Influences on the Health of Women and Children in Cabo Delgado, Mozambique: A Qualitative Study
Bibliographic record
Abstract
In 2017, the Government of Mozambique declared localized acute malnutrition crises in a range of districts across Mozambique including Cabo Delgado. This is in spite of intensive efforts by different non-governmental organizations (NGO) and the Government of Mozambique to expand access to information on good nutritional practices as well as promote nutrition-specific interventions, such as cooking demonstrations, home gardens and the distribution of micronutrient powder to children. This paper examines and discusses key nutritional influences on the health of pregnant and breastfeeding mothers in Cabo Delgado province, Mozambique. We conducted 21 key informant interviews (KIIs) with a wide range of stakeholders and 16 in-depth interviews (IDIs) with women. In addition, we conducted four focus group discussions with each of the following groups: (1) pregnant adolescent girls, (2) pregnant women >20 yrs, (3) women >20 yrs with babies <6 mths who were not practicing exclusive breastfeeding, (4) women >20 yrs of children <2 yrs and (5) with fathers of children <2 yrs. Data were analyzed thematically using NVIVO software. There is no single widely held influence on pregnant and breast-feeding women's nutritional decision-making, choices and food consumption. Rather, variables such as social-cultural, environmental, economic, gender, knowledge and information intersect in their roles in nutritional food choices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".