Adolescents who Perceive their Diet as Healthy Consume More Fruits, Vegetables and Milk and Fewer Sweet Drinks
Bibliographic record
Abstract
This study assessed whether adolescents’ perception of the healthfulness of their diet was related to dietary behaviors over the past week, controlling for demographic characteristics. Participants (n=391) completed an online survey on the frequency of specific dietary behaviors over the past week and the perceived healthfulness of their own diet compared to their peers’ diets. Mean intakes of juice, fruit, vegetables, milk, sugar-sweetened beverages, and diet beverages, were compared by perceived healthfulness of diet categories using analysis of covariance. Participants with higher perceived healthfulness of diet reported significantly higher mean fruit and vegetable intakes and a lower mean intake of sugar sweetened beverages over the past week than participants with the same or lower perceived healthfulness of diet (all p< 0.001). Participants who reported a higher perceived healthfulness of diet reported a significantly higher frequency of milk intake (p< 0.05) than those who reported the same perceived healthfulness of diet. Those with lower perceived healthfulness of diet reported higher mean frequencies of diet beverage intakes than those with higher perceived healthfulness (p<0.05). Further research should include qualitative studies with adolescents to explore how individuals rate their diets and how these perceptions influence dietary choices.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".