Impulsivity, Depressive Mood, and Cannabis Use in a Representative Sample of French-Speaking Swiss Young Men
Bibliographic record
Abstract
Cannabis is the most popular psychoactive substance under international regulations, with more than 192 million users worldwide. It has been associated with an addictive pattern of use and negative social and health-related outcomes in a subgroup of users. Consequently, understanding the individual differences that contribute to cannabis use and problematic use is of much importance. The current study examined the impact of impulsivity traits (negative urgency, positive urgency, lack of premeditation, lack of perseverance, sensation seeking), delay reward discounting, and depressive mood on cannabis use status during the past 6 months as well as problematic use of cannabis in a representative sample of 635 French-speaking Swiss young men recruited during their conscription in a Swiss national military recruitment center. Binary logistic and multiple linear regressions indicated that cannabis use status was significantly associated with greater depressive mood, elevated sensation seeking, and lack of perseverance, whereas problematic cannabis use was significantly related to higher depressive mood and steeper delay reward discounting. The present study highlights the importance of emotional symptoms in cannabis use and misuse. Our results also shed light on the potential psychological processes related to problematic consumption of cannabis and open avenues for preventive actions and psychological interventions that target problematic use of cannabis.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".