The Association Between Adolescents’ Food Literacy, Vegetable and Fruit Consumption, and Other Eating Behaviors
Bibliographic record
Abstract
Adolescents' intake of vegetables and fruits is generally low, and many demonstrate unhealthy eating behaviors. Food literacy may be key to improving adolescents' nutrition. However, the relationship between food literacy, fruit and vegetable intake, and other healthy eating behaviors remains unclear, as well as how these relationships may differ among boys and girls. This study assessed the relationship between food literacy (including food skills and cooking skills), vegetable and fruit consumption, and other eating behaviors of adolescents. This cross-sectional study included 1,054 students, including 467 boys and 570 girls from five francophone high schools in New Brunswick, Canada. Quantitative data on students' food and cooking skills, vegetable and fruit consumption, and other eating behaviors were collected with a self-reported questionnaire. Multilevel regressions were used to assess the relationship between food literacy, students' consumption of vegetables and fruits, and other eating behaviors. Better cooking skills were associated with healthier eating behaviors and greater vegetable and fruit consumption for boys and girls. Better food skills were also associated with healthier eating behaviors and greater vegetable and fruit consumption among both genders. These findings highlight the importance of improving food literacy among adolescents. Public health interventions should focus on increasing cooking and food skills to improve adolescents' nutrition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".