Le rôle médiateur de l'utilisation fréquente du cannabis durant l'âge adulte dans la relation entre les symptômes dépressifs durant l'adolescence et l'utilisation problématique du cannabis à l'âge de 35 ans
Bibliographic record
Abstract
Background: depressive symptoms are a major concern among adolescents because they cause a significant deterioration in quality of life. The association between depressive symptoms and problematic cannabis use has already been studied, but little is known about the mechanisms underlying this association. The aim of this study was therefore to investigate the mediating role of frequent cannabis use during adulthood in the relationship between depressive symptoms during adolescence and problematic cannabis use at the age of 35. Methods: data were drawn from the NDIT longitudinal study which recruited 1294 subjects in 1999-2000 in 10 Montreal high schools. We restricted the analysis to the 321 participants using cannabis at the age of 35. Our mediation model was estimated within the counterfactual framework to measure direct and indirect natural effects. A sensitivity analyse was conducted to observe the change in the estimates with a change in the modelling of the consumption frequency. Results: the total effect (TE) is significant and suggests that subjects with the highest score of depressive symptoms in adolescence have a significantly higher probability of having problematic use at age 35 (RD=0,34 [0,09 ; 0,59]) than subjects with the lowest level of depressive symptoms. The frequency of cannabis use significantly doesn’t mediate the association between depressive symptoms and problematic use (RD=0,16 [-0,09 ; 0,42]) but a direct effect has been found (RD=0,18 [0,03 ; 0,33]). Conclusion: this work suggests the importance of studying this subject in a larger cohort. Despite the absence of mediation in this study, frequency of use nevertheless appears to be a key mechanism in the association between depressive symptoms and problematic cannabis use.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".