A pilot randomized controlled trial of a digital cannabis harm reduction intervention for young adults with first-episode psychosis who use cannabis
Bibliographic record
Abstract
Cannabis use is widespread and associated with worsened prognosis for young adults with first-episode psychosis (FEP). Few cannabis harm reduction interventions have been evaluated for this population, despite potential to improve outcomes in those not ready for cannabis abstinence/reduction-focused interventions. This study aimed to determine a) the acceptability of a digital harm reduction intervention, the Cannabis Harm-reducing App to Manage Practices Safely (CHAMPS) and b) the feasibility of conducting a trial comparing FEP-specialized early intervention services (EIS)+CHAMPS versus EIS-only with this population. We conducted a multi-site pilot randomized controlled trial comparing both arms in 101 young adults (18 - 35 years old) with FEP using cannabis and attending EIS. Primary outcomes were trial retention rate (i.e., proportion of randomized participants retained at week 6; trial feasibility assessment) and CHAMPS completion rate (i.e., proportion of intervention participants completing four of six modules; CHAMPS acceptability assessment). Trial retention rate above 60 % indicated feasibility and completion rate above 50 % indicated acceptability. Additional outcomes included harm reduction strategy use, motivation to change cannabis behaviors, cannabis-related problems, cannabis use, psychotic symptoms and dependence severity, assessed at baseline, weeks 6, 12 and 18. Trial retention was 82.2 % and completion rate was 58.8 %, suggesting trial feasibility and CHAMPS acceptability. Signals of possible improvement in the intervention group were observed regarding harm reduction strategy use, motivation to change behaviors, cannabis-related problems and cannabis use frequency. This study supports conducting an efficacy trial assessing the potential of CHAMPS in improving outcomes for young adults with psychosis using 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".