Trends in Complementary Feeding Indicators in Children Aged 6–23 Months According to Participation in a Conditional Cash Transfer Program: Data from the Brazilian Food and Nutrition Surveillance System, 2015–2019
Bibliographic record
Abstract
Inadequate practices during complementary feeding are associated with malnutrition, especially in children experiencing vulnerable conditions and social inequality. The aim of this study was to evaluate the trends in complementary feeding indicators (CFIs) according to participation in a Brazilian cash transferu program—the Bolsa Família Program (BFP). This was a time-series study with secondary data from 600,138 children assisted from 2015 to 2019 and registered within the Brazilian Food and Nutrition Surveillance System. The CFIs assessed were food introduction, minimum meal frequency and appropriate consistency, minimum dietary diversity, iron-rich food, vitamin A-rich food, ultra-processed food consumption, and zero vegetable or fruit consumption. Prevalence and 95% confidence intervals were calculated for the CFIs according to BFP, the region of residence, and the child’s age. The Prais–Winsten regression method was used to analyze the temporal trend. There was a steady trend for all CFIs of a healthy diet. A decrease in ultra-processed food consumption for both BFP (−10.02%) and non-BFP children (−9.34%) was observed over the years. Children residing in the North and Northeast regions and those enrolled in the BFP were more distant from the recommended feeding practices when compared to the other regions and non-BFP children. The results highlight the relevance of nutritional surveillance and the need to improve food and nutrition public policies for children aged 6–23 months, particularly for those experiencing greater social vulnerability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".