Association Between Diet and Mental Health Outcomes in a Sample of 13,887 Adolescents in Canada
Bibliographic record
Abstract
Introduction: The high prevalence of mental disorders among adolescents calls for community-based and population-level prevention strategies. Diet is an important intervention target for primary prevention of mental disorders among adolescents. We used data from a large longitudinal study of Canadian adolescents (aged 14-18 y) to examine prospective associations between diet and mental health outcomes. Methods: We estimated the effect of diet (ie, consumption of vegetables and fruit and sugar-sweetened beverages [SSBs]) at baseline on depressive symptoms, anxiety symptoms, and psychological well-being (measured by the Center for Epidemiologic Studies Depression Scale-Revised, Generalized Anxiety Disorder 7 scale, and Flourishing Scale, respectively) and at 1-year follow-up in a sample of 13,887 Canadian secondary school students who participated in the 2017-2018 and 2018-2019 cycles of the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary (COMPASS) behavior study. We applied linear mixed-effects methods informed by a directed acyclic graph. Sensitivity analyses assessed the robustness of the effect estimates to unmeasured confounding variables. Results: Baseline SSB consumption was associated with greater severity of depressive (β = 0.04; 95% CI, 0.01-0.06) and anxiety (β = 0.02; 95% CI, 0-0.05) symptoms, particularly among male students, and poorer psychological well-being (β = -0.03; 95% CI, -0.05 to -0.01) at follow-up. Baseline vegetables and fruit consumption was positively associated with psychological well-being (β = 0.06; 95% CI, 0.03-0.10) but not other mental health outcomes at follow-up. Conclusion: Our results support the notion that diet should be part of comprehensive mental health prevention and promotion interventions to reduce the prevalence of mental health disorders among 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".