FEATURE ARTiclE. Multiple Health-Risk Behaviour and Psychological Distress in Adolescence
Bibliographic record
Abstract
Arbour-Nicitopoulos et al Objective: To examine the prevalence and correlates of psychological distress in a school-based sample of Canadian adolescents. Method: Self-reported data of demographics, weight status, physical activity, screen-time, diet, substance use, and psychological distress were derived from a representative sample of 2935 students in grades 9 to 12 (Mage = 15.9 years) from the 2009 Ontario Student Drug Use and Health Survey. Results: Overall prevalence of psychological distress was 35.1%. Significant associations were shown between psychological distress and the following: being female, tobacco use, not meeting physical activity and screen-time recommendations, and inadequate consumption of breakfast and vegetables. Conclusions: These findings highlight the need for targeting greater physical health promotion for adolescents at risk of mental health problems. Key words: adolescents, multiple health behaviour, mental health █ Résumé Objectif: Examiner la prévalence de la détresse psychologique et ses corrélats dans un échantillon scolaire d’adolescents canadiens et. Méthodologie: Les données démographiques auto-déclarées sur le poids, l’activité physique, le temps d’écran, l’alimentation, la consommation de drogue, et la détresse psychologique sont celles de 2935 élèves de 9e à 12e
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".