Energy, nutrients and food sources in snacks for adolescents and young adults
Bibliographic record
Abstract
OBJECTIVE: To evaluate associations between snacking and energy, nutrients and food source, and to identify the contribution of snacking across age, sex, weight status and lifestyle behaviors among adolescents and young adults. METHODS: A sub-sample was calculated from the population-based cross-sectional study 2015-Health Survey of São Paulo (ISA-Capital). The survey "ISA-Nutrição" used a sample of non-institutionalized individuals aged >15 years. For this study, only adolescents (12-18 years old; n=418) and young adults (19-29 years old; n=218) were included. Snacks were identified, and their contribution to energy, nutrients, and food sources were calculated. Descriptive statistics and logistic regressions were used. RESULTS: Participants experienced an average of 2.9±0.6 snacking occasions per day. Young adults consumed more energy from morning and night snacks, and adolescents, from afternoon snacks. The top three food sources on snacking contributed to 30.5% of energy: cookies (11.8%), sugar sweetened beverages (9.4%), sweets and other desserts (9.3%). Although results were non-significant, being a female (Odds Ratio [OR] 0.93; 95% confidence interval [95%CI] 0.36-1.49), meeting the physical activity recommendations (OR 0.75; 95%CI 0.25-1.25), and scoring higher for the healthy eating index (OR 0.88; 95%C 0.24-1.52) were all factors related to increased intake of snacks. Alternatively, overweight individuals (OR -0.54; 95%CI -1.00 to -0.08) consumed less snacks. CONCLUSIONS: Improving the quality of snacks should be considered in behavior-change strategies.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".