Profiles of cannabis users and impact on cannabis cessation
Bibliographic record
Abstract
Although cannabis was legalized in Canada in 2018 and is one of the most used substances in Canada, few studies have examined how individuals with different patterns of cannabis use differ in their attempts to decrease or abstain from cannabis. The current study examined how groups of cannabis users, which were formed on the basis of demographic characteristics, substance use patterns, mental health symptoms, and self-reported quality of life differed on their experiences with cannabis cessation. A sample of 147 Canadian adult participants who had attempted to decrease or quit cannabis were recruited from the community (n = 84, 57.14%) and crowdsourcing (n = 63, 42.86%). Four profiles of cannabis users emerged using a Latent Profile Analysis: low-risk (n = 62, 42.18%), rapidly escalating high-risk (n = 40, 27.21%), long-term high severity (n = 35, 23.81%), and long-term lower severity (n = 10, 6.80%). Individuals in the rapidly escalating profile had attempted to decrease their cannabis use more times compared to other profiles. More participants in the long-term high severity group found their use stayed the same or got worse after their last cessation attempt, compared to the low-risk group where more individuals indicated their use stopped. The results of the current study indicate that cannabis users differ in their attempts at reducing or ceasing cannabis use and that they may benefit from different intensity of cannabis interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".