Evaluation of a Cannabis Harm Reduction Intervention for People With First-Episode Psychosis: Protocol for a Pilot Multicentric Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Cannabis use is highly prevalent in young people with first-episode psychosis (FEP). Most report cannabis use and are often diagnosed with a cannabis use disorder upon admission to specialized services for psychosis. Cannabis use in this population is associated with worse clinical and psychosocial outcomes, rendering it an important clinical target. Despite this, few cannabis-specific interventions have been developed for FEP and empirically evaluated through randomized controlled trials. Most evaluated interventions have targeted cannabis abstinence, with limited efficacy, but none have centered on harm reduction outcomes for people with FEP who use cannabis. Early intervention services (EIS), the standard of care for FEP, have not successfully addressed problematic cannabis use in people with FEP either. Clinical trials are needed to explore the potential of harm reduction strategies, although these should be preceded by robust pilot studies to establish optimal design and approaches. OBJECTIVE: Recognizing the need for harm reduction strategies for individuals with FEP who use cannabis and based on research on patients' preferences supporting harm reduction interventions, we developed a mobile app-based cannabis harm reduction intervention for this population. This intervention is called Cannabis Harm-reducing Application to Manage Practices Safely (CHAMPS). Here, we describe the protocol for a multicenter, 2-arm, parallel group, randomized pilot trial evaluating the acceptability of CHAMPS for people with FEP who use cannabis and the feasibility of conducting a full-scale trial in this population using CHAMPS. The impact on key clinical outcomes will also be explored. METHODS: This pilot trial aims to recruit 100 young people with FEP using cannabis from 6 Canadian EIS clinics. Participants will be randomized in a 1:1 ratio to CHAMPS+EIS or EIS-only. CHAMPS acceptability will be assessed using completion rates for the intervention arm. Trial feasibility will be assessed using a retention rate for randomized participants. Secondary outcomes will explore tendencies of change in the use of protective behavioral strategies and in motivation to change strategies. Exploratory outcomes include cannabis use-related problems, other substance use, the severity of dependence, psychotic symptoms, and health care service use. RESULTS: Recruitment began in December 2021. Data collection and analysis are expected to be completed in early 2024. Study results describing CHAMPS acceptability and trial feasibility will then be submitted for publication in a peer-reviewed journal. CONCLUSIONS: CHAMPS uniquely combines evidence-based approaches, patient perspectives, and mobile health technology to support harm reduction in people with FEP who use cannabis. Attaining adequate acceptability and feasibility through this trial may justify further exploration of harm reduction tools, particularly within the context of conducting a larger-scale randomized controlled trial. This pilot trial has the potential to advance knowledge for researchers and clinicians regarding a feasible and user-acceptable research design in the cannabis and early psychosis fields. TRIAL REGISTRATION: ClinicalTrials.gov NCT04968275, https://clinicaltrials.gov/ct2/show/NCT04968275. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53094.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.086 | 0.017 |
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".