Using Best-Worst Scaling to assess preferences for online psychological interventions to decrease cannabis use in young adults with psychosis
Bibliographic record
Abstract
INTRODUCTION: In individuals with first episode psychosis (FEP) and cannabis use disorder (CUD), reducing cannabis use is associated with improved clinical outcomes. Access to evidence-based psychological interventions to decrease cannabis use in FEP clinics is highly variable; E-mental health interventions may help to address this gap. Development of E-interventions for CUD in individuals with FEP is in its incipient phases. OBJECTIVES: To assess preferences for online psychological interventions aiming at decreasing or stopping cannabis use in young adults with psychosis and CUD. METHODS: Individuals aged 18 to 35 years old with psychosis and CUD were recruited from seven FEP intervention programs in Canada and responded to an electronic survey between January 2020-July 2022. We used the Case 2 Best Worst Scaling methodology that is grounded in the trade-off utility concept to collect and analyse data. Participants selected the best or worst option for each of the nine questions corresponding to three distinct domains. For each domain we used conditional logistic regression and marginal models (i.e., three models in total) to estimate preferences for attributes (e.g., duration, frequency of online intervention sessions) and attribute levels (e.g., 15 minutes, every day). RESULTS: Participants (N=104) showed higher preferences for the following attributes: duration of online sessions; mode of receiving the intervention; method of feedback delivery and the frequency of feedback from clinicians (Table 1). Attribute-level analyses showed higher preferences for participating once a week in short (15 minutes) online interventions (Figure 1). Participants valued the autonomy offered by online interventions which aligns with their preference for completing the intervention outside the clinic and only require assistance once a week (Figure 2). Participants’ preferences were higher for receiving feedback related to cannabis consumption both from the application and clinicians at a frequency of once a week from clinicians (Figure 3). [Table: see text] Image: Image 2: Image 3: CONCLUSIONS: Using advanced methodologies to assess preferences, our results can inform the development of highly acceptable E-Mental health interventions for decreasing cannabis use in individuals with CUD and FEP. DISCLOSURE OF INTEREST: None Declared
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".