Eating Away from Home: Influences on the Dietary Quality of Adolescents with Overweight or Obesity
Bibliographic record
Abstract
PURPOSE: To examine the influence of peers and the source of meals and snacks on the dietary quality of adolescents seeking obesity treatment. METHODS: Baseline surveys were completed by 173 adolescents with overweight or obesity (11-16 years old) enrolled in an e-health intervention in Vancouver, British Columbia. Dietary quality was assessed with three 24-h dietary recalls used to compute a Healthy Eating Index adapted to the Canadian context (HEI-C). Multiple linear regression examined associations between HEI-C scores and the frequency of: (i) meals prepared away from home, (ii) purchasing snacks from vending machines or stores, (iii) eating out with friends, and (iv) peers modeling healthy eating. RESULTS: Adolescents reported eating approximately 3 lunch or dinner meals prepared away from home and half purchased snacks from vending machines or stores per week. After adjusting for socio-demographics, less frequent purchases of snacks from vending machines or stores (b = -3.00, P = 0.03) was associated with higher HEI-C scores. More frequent dinner meals prepared away from home and eating out with friends were only associated with lower HEI-C scores in unadjusted models. CONCLUSIONS: Snack purchasing was associated with lower dietary quality among obesity treatment-seeking adolescents. Improving the healthfulness of foods obtained away from home may contribute to healthier diets among these adolescents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".