Identifying Eggs and Small Fish as Priority Foods in Rural Tanzania for Pregnant and Lactating Women, Infants and Young Children: Formative Research for the ENRICH Project (P10-144-19)
Bibliographic record
Abstract
A diverse diet including animal source foods (ASF) is essential for pregnant and lactating women, as well as infants and young children (IYC) 6–23 months to promote healthy growth. In rural impoverished areas of Tanzania, where stunting prevalence is high and dietary diversity low, the Enhancing Nutrition Services to Improve Maternal and Child Health (ENRICH) project aims to improve the health and nutrition of women and IYC. To identify nutritious priority foods and behaviors to promote in this context, formative research was conducted. Our objective was to identify priority ASF to promote increased dietary diversity and improved maternal and IYC nutrition within the local context. Using a social-ecological framework and an ethnographic approach, we employed qualitative methods to collect data from purposive samples on potential key foods and behaviors. Food attributes, market surveys, household food stock, in-depth interviews, 24-hour dietary recalls and home observations were conducted. Analysis of each method and triangulation of results led to selected ASF foods and behaviors, which were tested in household trials for final recommendations. Among the various ASF explored, eggs and small fish emerged with the greatest potential for increased consumption. Many positive attributes of eggs were voiced, but some negative attributes, primarily related to traditional cultural views, were also expressed. Small fish were associated with mostly positive attributes. Compared with other ASF, eggs and small fish were commonly available and considered inexpensive. Interviews with pregnant women and parents of IYC demonstrated links among nutritious foods, healthy growth and future aspirations, yet dietary recalls showed poor dietary quality and low ASF intake. Results from household trials found eggs available, easy to prepare and acceptable by women and IYC. Similar results were found for small fish. Formative research provided context rich data from multiple perspectives to select priority ASF. Household trials provided further evidence of practical feasibility and acceptability, as well as improved understanding of how to promote egg and small fish consumption. This research informed the development of messaging and a behavior change strategy for this setting. Global Affairs Canada, World Vision Canada, Nutrition International.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".