Sugar-Sweetened Beverage Intake among Chilean Preschoolers and Adolescents in 2016: A Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Chile has the highest sugar-sweetened beverage (SSB) sales of any country and a growing burden of childhood obesity. This study examines SSB intake in Chilean children after a 5% SSB tax increase in 2014 but prior to marketing, labeling, and school policies implemented in 2016. METHODS: = 770) from low⁻moderate income neighborhoods. Beverages were categorized as regulated or unregulated according to whether they exceeded nutrient thresholds established by the 2016 policies. RESULTS: Preschoolers consumed mainly beverage calories from regulated dairy beverages and substitutes (109 kcal, SD 30), unregulated dairy beverages (102 kcal, SD 24), and regulated fruit and vegetables drinks (44 kcal, SD 20). For adolescents, the greatest contributions came from regulated sodas (77 kcal, SD 47), regulated dairy beverages and substitutes (41 kcal, SD 16), and unregulated coffee and tea (41 kcal, SD 11). Overall, regulated beverages provided a greater proportion of calories than unregulated for preschoolers (15.0% vs. 11.8%) and for adolescents (9.1% vs. 5.0%). CONCLUSIONS: Before major policy implementation, regulated beverages accounted for a higher percentage of energy intake than unregulated beverages among both age groups. Future research will be needed to evaluate the impact of Chile's new policies on sugary beverage intake in children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".