Technology-Based Psychotherapeutic Interventions for Decreasing Cannabis Use in People with Psychosis: A Systematic Review Update
Bibliographic record
Abstract
Cannabis use is highly prevalent in people with psychotic disorders and is associated with adverse outcomes. We updated our 2020 systematic review related to the efficacy of technology-based psychological interventions (TBPIs) to decrease cannabis use in individuals with psychosis, the design of TBPIs, and their acceptability. We searched Medline, PubMed, Embase, CINAHL, PsycINFO, and EMB Reviews for references indexed between November 27, 2019, and July 27, 2023, and used the PRISMA guidelines to report the results. We screened 5083 unique records and retained three studies for the narrative synthesis. Two quantitative studies showed promising results of internet or virtual reality-based psychological interventions that incorporate cognitive behavioral therapy, motivational interviewing, and psychoeducation principles on the frequency and quantity of cannabis use. A qualitative exploratory study provided an integrative synthesis of patient and clinician opinions pertaining to the use of psychological approaches and technology to tackle cannabis misuse in individuals with psychosis. In contradiction with the rapidly expanding mobile-health solutions in the field of mental health, there is a dearth of research related to the use of internet and app-based psychological interventions for cannabis use in individuals with psychosis. The use of qualitative research is pivotal in the development of TBPIs. Our initial review and its update show that only 11 peer-reviewed journal articles that met our inclusion criteria have been published so far.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".