A moderated mediation analysis of the association between smoking and suicide attempts among adolescents in 28 countries
Bibliographic record
Abstract
Globally, evidence has shown that many adolescents are victims of substance use, mainly cigarette smoking, and it has been associated with suicidal ideation. However, the mechanisms underlying this association are poorly understood. This study examines whether truancy mediates and gender moderates the association of cigarette smoking with suicide attempts among adolescents in 28 countries. Data from the Global School-Based Student Health Survey were used. Hierarchical multiple logistic regression analyses were used to estimate the effect-modification of gender on cigarette smoking and suicide attempt. The mediating effect of truancy on the association between cigarette smoking and suicidal attempt was assessed using the generalized decomposition method. Cigarette smoking was associated with suicide attempts after adjusting for several confounding variables (aOR = 1.21; 95% CI = 1.09-1.33). The bootstrap results from the generalized decomposition analysis indicated that truancy partially mediated the association of cigarette smoking with a suicide attempt, contributing 21% of the total effect among in-school adolescents. Hierarchical regression analyses suggested that gender moderated the effect of cigarette smoking on suicidal attempts: female adolescents who smoked had 36% higher odds of suicidal attempts compared to male adolescents. The findings suggest possible pathways for designing and implementing interventions to address adolescents' cigarette smoking and truancy to prevent suicidal attempts.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".