Harm reduction interventions as a potential solution to managing cannabis use in people with psychosis: A call for a paradigm shift
Bibliographic record
Abstract
Cannabis consumption is widespread in individuals with psychosis, with 60% of individuals with psychosis reporting lifetime cannabis use ( Colizzi et al., 2018 ).Continued cannabis use after a first episode of psychosis is associated with more psychotic relapses, more severe psychotic episodes, and longer hospitalizations compared to people with psychosis who stopped using or never used cannabis ( Schoeler et al., 2016 ).We want to highlight the critical need to complement existing treatment options with harm reduction strategies to help improve the well-being and prognosis for people with psychosis who use cannabis.Many health guidelines and scientific literature nominate cannabis cessation or reduction in individuals with psychosis ( Fischer et al., 2022 ), and interventions and therapeutic approaches for cannabis use primarily focus on abstinence and use reduction.Cannabis abstinence or at least reduction are theoretically the ideal intervention targets for people with psychosis who use cannabis and seek help.When achieved, these targets are associated with better clinical outcomes in people with psychosis ( Schoeler et al., 2016 ).Unsurprisingly, targeting abstinence or reduction aligns with what is largely promoted in clinical practice.Further, addiction service providers and health practitioners tend to support abstinence-oriented prevention measures and treatment goals ( Ekendahl et al., 2018 ;Rosenberg et al., 2020 ).Despite guideline recommendations and clinical practice favoring abstinence or use reduction, there are few known effective interventions to achieve these targets.Systematic reviews examining cannabis pharmacological or psychosocial interventions for people with severe mental illness report having insufficient evidence to support any one intervention over another for cannabis cessation or reduction outcomes, with overall relatively limited efficacy of these interventions for reducing cannabis use ( Hunt et al., 2019 ;Temmingh et al., 2018 ).The current state of research may leave health practitioners with limited guidance about which interventions to offer and may leave many people with psychosis and ongoing cannabis use with inadequate support.A potential complementary and obvious alternative option for people with psychosis who use cannabis and seek help is harm reduction.In past decades, harm reduction interventions have been accepted as a critical part of the therapeutic arsenal for many substance use disorders.Rather than focusing on ceasing use completely or reducing use, harm reduction approaches seek to reduce the negative consequences
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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 | 0.037 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.034 | 0.044 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".