Étude du rôle médiateur de la consommation de cannabis dans la relation entre la régulation des émotions et la santé mentale
Bibliographic record
Abstract
Background: emotion regulation is known to play a role in mental health, since better emotion regulation is thought to have a beneficial effect on mental health. Emotion regulation is also linked to the use of substances such as alcohol or cannabis: poorer emotion regulation suggests a greater tendency to use these substances. Some studies have documented the effects of cannabis use as a risk factor for depressive and anxiety symptoms. Objective: to investigate the mediating effect of cannabis use in the relationship between emotion regulation and mental health. Methods: we used data from the Nicotine Dependence in Teens Study (inception 1999-2000), that has followed 1294 participants recruited from 10 Montreal secondary schools. Our analyses include 665 subjects who responded to both data collections carried out when they were 34 and 36 years old. The counterfactual approach was used to carry out the mediation analysis. This decomposes the total effect of emotion regulation on mental health into a direct effect and an indirect effect via cannabis use. Results: The estimation of the indirect effect shows no mediating effect of cannabis consumption in the relationship between emotion regulation and mental health, whether for the crude, semi-adjusted or adjusted analysis. The estimation of the direct effect shows an influence of emotion regulation on mental health, supporting previous findings in the literature.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".