Dietary diversity and social determinants of nutrition among late adolescent girls in rural Pakistan
Bibliographic record
Abstract
The conditions in which adolescent girls mature shape their health, development and nutrition. Nutrient requirements increase to support growth during adolescence, but gaps between consumption and requirements exist in low- and middle-income countries. We aimed to identify and quantify the relationship between dietary intake and diverse social determinants of nutrition (SDN) among a subset of adolescent girls 15-18.9 years (n = 390) enrolled within the Matiari emPowerment and Preconception Supplementation (MaPPS) Trial. The primary outcome, dietary diversity score (DDS), was derived by applying the Minimum Dietary Diversity for Women 10-item scale to 24-h dietary recall data collected three times per participant. To examine the associations between the SDN-related explanatory variables and DDS, we generated a hierarchical, causal model using mixed effects linear regression to account for the cluster-randomized trial design. Using all data, diets lacked diversity (DDS mean ± SD: 3.35 ± 1.03 [range: 1-7; n = 1170]), and the minimum cut-off for dietary diversity was infrequently achieved (13.5%; 95% CI: 11.6-15.6%). Consumption of starches was reported in all recalls, but micronutrient-rich food consumption was less common. Of the SDN considered, wealth quintile had the strongest association with DDS (P < 0.0001). The diets of the sampled Pakistani adolescent girls were insufficient to meet micronutrient requirements. Poverty was the most important predictor of a diet lacking in diversity, indicating limited purchasing power or access to nutritious foods. Dietary diversification and nutrition education strategies alone are unlikely to lead to improved diets without steps to tackle this barrier, for example, through fortification of staple foods and provision of supplements.
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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 | 0.000 | 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 | 0.000 |
| 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".