Fruit and vegetable intake among Emirati adolescents: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Interventions to promote healthy eating in adolescents are needed in the United Arab Emirates. To design effective interventions, adolescent eating behaviours need to be understood. AIMS: This study aimed to describe eating behaviours of adolescents in Dubai and the factors associated with fruit and vegetable intake. METHODS: This was a sequential explanatory study using a mixed methods approach. Ten of the 34 Arabic high schools in Dubai were randomly selected and students in grades 10-12 were included. Data were collected on self-reported fruit and vegetables intake, eating behaviours, food availability and sociodemographic variables. In the qualitative phase, 14 students were interviewed about their eating behaviour. RESULTS: A total of 620 students were included: 57% were boys and most reported medium/high family affluence. Only 28% of the participants met the recommended daily fruit and vegetable intake, with significantly more males than females meeting it (P < 0.01). Lunch was the most frequently eaten meal, breakfast was frequently skipped, and there were high levels of fast food and soft drink consumption. Adequate fruit and vegetable intake was positively associated with increased lunch frequency, being male, parental support for healthy eating, and positive perception of family meals. CONCLUSIONS: There are significant differences in eating habits between Emirati male and female adolescents. Lunch, as the main family meal, faces threats because of modern working hours. The gender-specific social context may require targeted interventions to achieve optimal outcomes in each group.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".