Evaluating preferences for online psychological interventions to decrease cannabis use in young adults with psychosis: An observational study
Bibliographic record
Abstract
Innovative technology-based solutions have the potential to improve access to clinically proven interventions for cannabis use disorder (CUD) in individuals with first episode psychosis (FEP). High patient engagement with app-based interventions is critical for achieving optimal outcomes. 104 individuals 18 to 35 years old with FEP and CUD from three Canadian provinces completed an electronic survey to evaluate preferences for online psychological intervention intensity, participation autonomy, feedback related to cannabis use, and technology platforms and app functionalities. The development of the questionnaire was informed by a qualitative study that included patients and clinicians. We used Best-Worst Scaling (BWS) and item ranking methodologies to measure preferences. Conditional logistic regression models for BWS data revealed high preferences for moderate intervention intensity (e.g., modules with a length of 15 min) and treatment autonomy that included preferences for using technology-based interventions and receiving feedback related to cannabis use once a week. Luce regression models for rank items revealed high preferences for smartphone-based apps, video intervention components, and having access to synchronous communications with clinicians and gamification elements. Results informed the development of iCanChange (iCC), a smartphone-based intervention for the treatment of CUD in individuals with FEP that is undergoing clinical testing.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".