Changes in Children’s Adherence to Sustainable Healthy Diets During the Implementation of Chile’s Food Labelling and Advertising Law: A Longitudinal Study (2016–2019)
Bibliographic record
Abstract
Objectives: This longitudinal study measured changes in adherence to sustainable healthy diets in 698 Chilean children (aged 3–6 years at baseline) over the period that Chile’s Food Labelling and Advertising Law was implemented. Methods: Dietary data were collected annually from 2016 to 2019 applying single multiple-pass 24 h dietary recalls to children’s primary caretakers. The Planetary Health Diet Index for Children and Adolescents (PHDI-C) was used to quantify adherence to sustainable healthy diets where higher scores indicate better adherence. Linear mixed models were fitted to estimate the change in PHDI-C total and individual component scores from 2016 to 2019. Results: Mean total PHDI-C score decreased from 50.1 points in 2016 to 46.3 and 46.1 in 2018 and 2019, respectively (p-value < 0.001), suggesting that children’s overall adherence to sustainable healthy diets was low and decreased over time. Intake of legumes, fruits, dark green vegetables, red and orange vegetables, and vegetable oils decreased, while intake of palm oil, red meats, and animal fats increased, resulting in small but significant declines in eight PHDI-C component scores. Whole cereal intake increased, while the consumption of dairy products and added sugars decreased, resulting in improvements in three PHDI-C component scores. Conclusions: Aside from the decrease in added sugar intake, all dietary changes observed in this study were consistent with trends described among children transitioning from pre-school age to school age. The Law might have contributed to reducing children’s added sugar intake, but further research is required to establish causality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".