Psychosocial Interventions in Reducing Cannabis Use in Early Phase Psychosis
Bibliographic record
Abstract
OBJECTIVE: Cannabis use in people with early phase psychosis (EPP) can have a significant impact on long-term outcomes. The purpose of this investigation was to describe current cannabis use treatment practices in English-speaking early intervention services (EISs) in Canada and determine if their services are informed by available evidence. METHOD: Thirty-five Canadian English-speaking EISs for psychosis were approached to complete a survey through email, facsimile, or online in order to collect information regarding their current cannabis use treatment practices. RESULTS: Data were acquired from 27 of the 35 (78%) programs approached. Only 12% of EISs offered formal services that targeted cannabis use, whereas the majority (63%) of EISs offered informal services for all substance use, not specifically cannabis. In programs with informal services, individual patient psychoeducation (86%) was slightly more common than individual motivational interviewing (MI) (76%) followed by group patient psychoeducation (52%) and information handouts (52%). Thirty-seven percent of EISs offered formal services for substance use, and compared to programs with informal services, more MI, cognitive-behavioural therapy, and family services were offered, with individual treatment modalities more common than groups. No EISs used contingency management, even though it has some preliminary evidence in chronic populations. Evidence-based service implementation barriers included appropriate training and administrative support. CONCLUSIONS: While most English-speaking Canadian EIS programs offer individual MI and psychoeducation, which is in line with the available literature, there is room for improvement in cannabis treatment services based on current evidence for both people with EPP and their families.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".