Fruit and vegetable consumption in adolescence and health in early adulthood: a longitudinal analysis of the Statistics Canada’s National Population Health Survey
Bibliographic record
Abstract
BACKGROUND: The present study aimed to explore a longitudinal relationship between fruit and vegetable consumption in adolescence and two health-related outcomes (i.e., self-rated health and mental health) in early adulthood in the community. METHODS: Data from a longitudinal cohort of the Canadian National Population Health Survey (NPHS) were used. Participants of the 2002/03 survey aged 15-17 years old were followed and surveyed in 2008/09. The number of the sample used in the statistical analyses was 250 (n=250). Multiple logistic regression analyses were used to assess the associations of fruit and vegetable consumption in the adolescence (classified into tertiles) with non-excellent (or poor) self-rated health and poor mental health (defined as having a K6 score of 5+) at follow-up. RESULTS: After adjusting for sex, age, the highest level of education in household, and the other covariates, participants who consumed fruits and vegetables most frequently at baseline had a significantly smaller odds ratio for being non-excellent self-rated health (OR 0.30, 95% CI 0.11, 0.83). No significant associations were found between fruit and vegetable consumption at baseline and poor mental health at follow-up in any model (p>0.05). CONCLUSIONS: The results of this longitudinal study suggest that high fruit and vegetable consumption in adolescence has a beneficial influence on self-rated health in the early adulthood.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".