Exploring the Role of Emotion Dysregulation and Coping Motives on Cannabis-related Consequences: A Daily Diary Study among Emerging Adults
Bibliographic record
Abstract
Emerging adults (EAs; ages 18-25) have the highest rates of cannabis use and cannabis-related consequences relative to other age groups. There is substantial evidence pointing to emotion dysregulation and coping motives as important variables impacting cannabis-related consequences, however, current research is limited in examining their collective impact. The current study sought to explore trait-level facets of emotion dysregulation and state-level coping motives to determine their influence on cannabis-related consequences among EAs. Data were collected daily from 64 cannabis-using EAs who reported their cannabis use, motives, and consequences over 30 days. Emotion dysregulation was assessed at baseline. A mediation model was used to examine whether coping motives influence the relationship between emotion dysregulation and cannabis use. Hierarchical linear modelling tested whether emotion dysregulation facets moderated the within-person relationship between coping motives and cannabis problems. Findings highlight two significant facets of emotion dysregulation, Nonacceptance of Emotional Responses and Limited Access to Emotion Regulation Strategies, as contributing to coping-motivated cannabis use and subsequent cannabis-related consequences. These results help elucidate the underlying mechanisms that protect EAs from cannabis-related consequences and can be used to develop effective screening tools and interventions to help prevent problematic cannabis consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".