Harm reduction interventions for young people with first-episode psychosis who continue to use cannabis
Bibliographic record
Abstract
Background: In young people with a first episode of psychosis (FEP), cannabis use is widespread and associated with a significantly worsened prognosis. Few cannabis-specific interventions for this population have been evaluated; most have focused on ceasing or reducing cannabis use, and none are considered highly effective in addressing cannabis use. For many young people with FEP who use cannabis, abstinence-focused approaches may be unappealing or unrealistic options potentially impacting intervention engagement or outcomes. Harm reduction interventions, which seek to reduce cannabis use-related harms rather than requiring abstinence, may present an appealing alternative to young people with FEP who continue using cannabis; few interventions have implemented this approach. Further, there is a scarcity of studies documenting preferences of young people with FEP for cannabis harm reduction interventions or evaluating cannabis harm reduction interventions in this population. Aims: This thesis aims to (a) synthesize the evidence on efficacy of preventive interventions focusing on cannabis use for people with psychosis (review); (b) determine the preferences of young people with FEP using cannabis in relation to key characteristics of cannabis harm reduction interventions (survey); and (c) describe the development of a technology-based harm reduction intervention for young people with FEP who use cannabis, and its associated pilot trial (protocol). Methods: Three studies were conducted. Review: Six databases were searched for randomized controlled trials (RCTs) of interventions aiming to reduce cannabis use-related harms or prevent cannabis use disorder in people with psychosis. Two independent reviewers assessed eligible studies for effectiveness and reporting quality. Intervention effectiveness was described by assessing cannabis-related harms and use outcomes. Survey: A survey, combining two discrete choice experiments (DCE) and conventional survey methodology, was developed to document preferences for cannabis harm reduction interventions. This survey was administered to 89 youth in Canada having FEP and using cannabis. One DCE focused on core attributes of harm reduction interventions (DCE 1) and the second on attributes of boosters (DCE 2). We analyzed these using mixed ranked-ordered logistic regression models. Conventional survey questions on preferences were analyzed using summary statistics. Protocol: A brief, technology-based cannabis harm reduction intervention for young people with FEP using cannabis, called the Cannabis Harm- reducing App to Manage Practices Safely (CHAMPS), was developed to complement FEP standard care. A pilot RCT aiming to recruit 100 young people with FEP and using cannabis was designed to assess the intervention acceptability and the feasibility of conducting a full-scale trial in this population and with this intervention. Results: Review: Five studies were assessed, none of which measured cannabis use-related harms or demonstrated clear efficacy in reducing cannabis use in young people with psychosis. All studies had high risk of bias. Survey: Preferred characteristics for cannabis-focused harm reduction interventions (DCE 1) were: shorter sessions; less frequent sessions; shorter interventions; and technology-based interventions. Preferences for post-intervention boosters (DCE 2) included opting into boosters and having shorter boosters. Protocol: The protocol describing the development of CHAMPS and its pilot RCT was published; the pilot RCT is currently underway. Significance: Few cannabis-specific interventions for young people with FEP have been conducted, none demonstrating clear efficacy or focusing on harm reduction outcomes. Survey findings suggest an interest in cannabis harm reduction interventions and highlight preferred characteristics of young people with FEP for cannabis harm reduction interventions. These findings can guide the design of cannabis harm reductions interventions, as with CHAMPS. CHAMPS represents a novel cannabis harm reduction intervention for young people with FEP who use cannabis, and its associated pilot RCT has the potential to advance knowledge for scientists regarding acceptability and feasibility of implementing cannabis harm reduction interventions in the cannabis and early psychosis fields.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".