Preferences of Young Adults With Psychosis for Cannabis-Focused Harm Reduction Interventions: A Cross-Sectional Study: Préférences des jeunes adultes souffrant de psychose pour les interventions de réduction des méfaits axées sur le cannabis : une étude transversale
Bibliographic record
Abstract
ObjectivesCannabis use is common in people with early-phase psychosis (EP) and is associated with worse treatment outcomes. Few targeted interventions for cannabis use behaviour in this population exist, most focusing on abstinence, none focusing on harm reduction. Many people with EP will not seek treatment for their cannabis use with current therapeutic options. Understanding preferences for cannabis-focused harm reduction interventions may be key to improving outcomes. This study aimed to determine preferences of young adults with EP who use cannabis for cannabis-focused harm reduction interventions.MethodsEighty-nine young adults across Canada with EP interested in reducing cannabis-related harms were recruited. An online questionnaire combining conventional survey methodology and two unique discrete choice experiments (DCEs) was administered. One DCE focused on attributes of core harm reduction interventions (DCE 1) and the second on attributes of boosters (DCE 2). We analysed these using mixed ranked-ordered logistic regression models. Preference questions using conventional survey methodology were analysed using summary statistics.ResultsPreferred characteristics for cannabis-focused harm reduction interventions (DCE 1) were: shorter sessions (60 min vs. 10 min, odds ratio (OR): 0.72; P < 0.001); less frequent sessions (daily vs. monthly, OR: 0.68; P < 0.001); shorter interventions (3 months vs. 1 month, OR: 0.80; P < 0.01); technology-based interventions (vs. in-person, OR: 1.17; P < 0.05). Preferences for post-intervention boosters (DCE 2) included opting into boosters (vs. opting out, OR: 3.53; P < 0.001) and having shorter boosters (3 months vs. 1 month, OR: 0.79; P < 0.01). Nearly half of the participants preferred to reduce cannabis use as a principal intervention goal (vs. using in less harmful ways or avoiding risky situations).ConclusionsFurther research is required to see if technology-based harm reduction interventions for cannabis featuring these preferences translate into greater engagement and improved outcomes in EP patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".