Recovery from cannabis use disorders: Abstinence versus moderation and treatment-assisted recovery versus natural recovery.
Bibliographic record
Abstract
The present study of recovery from cannabis use disorders was undertaken with 2 primary objectives that address gaps in the literature. The first objective was to provide an exploratory portrait of the recovery process from cannabis use disorders, comparing individuals who recovered naturally with those who were involved in treatment. The second objective was to explore systematically the similarities and differences between abstinence and moderation recoveries. Adults who have recovered from a cannabis use disorder were recruited in the community (N = 119). The abstinence and treatment-assisted participants exhibited higher levels of lifetime cannabis problem severity than the moderation and natural recovery participants, respectively. As well, cognitive factors were identified as the most useful strategies for recovery (e.g., thinking about benefits and negative consequences of cannabis), followed by behavioral factors (e.g., avoidance of triggers for use and high-risk situations). Findings lend further support to the effectiveness of cognitive, motivational, and behavioral strategies as helpful actions and maintenance factors involved in the recovery process. The findings also generally support the idea that cannabis use disorders lie on a continuum of problem severity, with moderation and natural recoveries more likely to occur at the lower end of the continuum and abstinence and treatment-assisted recoveries more likely to occur at the upper end.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".