Technology-Based Psychological Interventions for Young Adults With Early Psychosis and Cannabis Use Disorder: Qualitative Study of Patient and Clinician Perspectives
Bibliographic record
Abstract
BACKGROUND: The persistence of cannabis use disorder (CUD) in young adults with first-episode psychosis (FEP) is associated with poor clinical and functional outcomes. Face-to-face psychological interventions are effective in treating CUD. However, their use in early intervention services (EISs) for psychosis is inconsistent because of barriers, including high workload and heterogeneity in training of clinicians and lack of motivation for treatment among patients. Tailoring new technology-based psychological interventions (TBPIs) to overcome these barriers is necessary to ensure their optimal acceptability. OBJECTIVE: The aim of this study is twofold: to explore psychological intervention practices and intervention targets that are relevant for treating CUD in individuals with early psychosis and to explore factors related to the development and implementation of a technology-assisted psychological intervention. METHODS: A total of 10 patients undergoing treatment for FEP and CUD in EISs participated in a focus group in June 2019. Semistructured individual interviews were conducted with 10 clinicians working in first-episode clinics in the province of Québec, Canada. A hybrid inductive-deductive approach was used to analyze data. For the deductive analysis, we used categories of promoting strategies found in the literature shown to increase adherence to web-based interventions for substance use (ie, tailoring, reminders, delivery strategies, social support, and incentives). For the inductive analysis, we identified new themes through an iterative process of reviewing the data multiple times by two independent reviewers. RESULTS: Data were synthesized into five categories of factors that emerged from data collection, and a narrative synthesis of commonalities and differences between patient and clinician perspectives was produced. The categories included attitudes and beliefs related to psychological interventions (eg, behavioral stage of change), strategies for psychological interventions (eg, motivational interviewing, cognitive behavioral therapy, psychoeducation, stress management), incentives (eg, contingency management), general interest in TBPIs (eg, facilitators and barriers of TBPIs), and tailoring of TBPIs (eg, application needs and preferences, outcome measures of interest for clinicians). CONCLUSIONS: This study provides a comprehensive portrait of the multifaceted needs and preferences of patients and clinicians related to TBPIs. Our results can inform the development of smartphone- or web-based psychological interventions for CUD in young adults with early psychosis.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".