Women’s dietary diversity changes seasonally in Malawi and Zambia
Bibliographic record
Abstract
Objective: There is growing recognition of the role that seasonality plays in agricultural production, expenditure, food security, diet quality and nutritional status, however, annual or bi-annual surveys may not capture seasonal or intra-seasonal shifts in dietary intake which can inform agriculture and nutrition policies, programming, and monitoring and evaluation. Design, Setting and Participants: Seasonal variation in diets of women of reproductive age (WRA) living in rural Malawi and Zambia were measured bimonthly for eleven rounds, from September 2017 to May 2019. Trained enumerators collected data on a sample of 200 women using a qualitative 24-hour list-based recall of food items consumed, based on the ten food groups for Minimum Dietary Diversity of Women (MDD-W). Results and Conclusion: There were significant seasonal fluctuations in the percentage of women achieving MDD-W, ranging from a low of 18% to a high of 82%. MDD-W followed expected fluctuations, peaking during harvest season and lowering during lean season, however, there were unexpected highs and lows at other times, demonstrating the importance of regular monitoring. The study demonstrated significant seasonal fluctuations in the proportion of WRA achieving MDD-W, having implications for project monitoring and evaluation. The research provides evidence of periods of abundance and scarcity for nutritionally important food groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".