Cannabis Use Motives in Emerging Adulthood: Using Daily Diary Method to Inform Intervention
Bibliographic record
Abstract
The increasing rate of cannabis use among emerging adults is a growing public health concern in Canada. Research has shown that frequent cannabis use is associated with a myriad of negative mental health outcomes. However, little is known about the motivations or reasons behind such use in emerging adulthood. Daily diary data provides information on both the proximal and distal factors for cannabis use, which can inform interventions to prevent and treat problematic use in emerging adulthood. The first phase of the study assessed four trait motives (coping, sleep, enjoyment, and celebration) that were hypothesized to moderate the daily relationships between negative and positive affect and cannabis use and related consequences. A sample consisting of 63 emerging adults (ages 18-25) recruited from the community completed baseline questionnaires followed by 14 daily reports on mood, motives, cannabis use, and consequences. Across the 14 days, daily negative mood was associated with greater cannabis consequences, although for those who use cannabis to cope, the strength of this relationship was greater on days when participants reported less negative mood. Enjoyment motives moderated the relationship between daily positive mood and consequences, such that the relationship was even stronger on days when participants experienced less positive mood, acting as a buffer against cannabis-related consequences. The second phase of the study investigated the impact of using a daily diary as a self-monitoring tool to enhance motivation to change cannabis use. Additionally, at the end of the daily diary period, participants were randomly assigned to either receive a brief personalized feedback intervention or to a control condition. Although there were no differences in cannabis use or consequences between the intervention and control groups, men exhibited a significant reduction in cannabis consequences from baseline to follow-up, supporting the efficacy of self-monitoring via an electronic device as an intervention tool. Overall, findings highlight that the relationship between daily mood and cannabis motives contribute to daily cannabis consequences and ought to be considered when developing prevention and interventions for emerging adults.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".