Behind the plate: revealing the drivers of women's food choices in Senegal
Bibliographic record
Abstract
Women’s eating practices in Senegal are shaped by intersecting economic, cultural, and environmental factors that contribute to the growing burden of diet-related non-communicable diseases. This study examines key determinants of food choices among women across income levels and urban–rural settings, using a mixed-methods design based on the 2021 National Survey on Food Consumption in Senegal conducted by Consortium pour la Recherche Economique et Sociale. The quantitative analysis included 1764 women, while the qualitative component comprised 31 focus group discussions and 6 in-depth interviews. Quantitative findings indicate that freshness, hygiene, and price are primary purchase criteria, with freshness cited by 88% of women in the richest quintile versus 60% in the poorest (χ 2 = 92.59, p < 0.001; Cramér’s V = 0.227), showing a moderate effect of income. Hygiene followed a similar trend (Cramér’s V = 0.156), whereas price was universally cited (Cramér’s V = 0.03). Women in lower-income households favour affordable, energy-dense foods, while wealthier women prioritize taste, convenience, and packaging. Qualitative analyses further reveal gender roles, cultural and religious taboos, and advertising shape choices, creating implicit pressure to prepare flavourful meals despite health risks. Media exposure enhances the appeal of ultra-processed products as symbols of modernity. Urban–rural disparities in food availability influence dietary diversity and nutritional outcomes. These findings underscore the complexity of food decision-making and highlight the need for culturally grounded, context-sensitive interventions that promote nutritional awareness and healthier diets tailored to socioeconomic realities and women’s central role in household food provisioning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".