Preferences for research design and treatment of comorbid depression among patients with an opioid use disorder: A cross-sectional discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Up to 74 % of people with an opioid use disorder (OUD) will experience depression in their lifetime. Understanding and addressing the concept of preference for depression treatments and clinical trial designs may serve as an important milestone in enhancing treatment and research outcomes. Our goal is to evaluate preferences for depression treatments and clinical trial designs among individuals with an OUD and comorbid depression. METHODS: We evaluated preferences for depression treatments and clinical trial designs using an online cross-sectional survey including a best-best discrete choice experiment. We recruited 165 participants from opioid agonist treatment clinics and community-based services in Calgary, Charlottetown, Edmonton, Halifax, Montreal, Ottawa, Quebec City, St. John's and Trois-Rivières, Canada. RESULTS: Psychotherapy was the most accepted (80.0 %; CI: 73.9-86.1 %) and preferred (31.5 %; CI: 24.4-38.6 %) treatment. However, there was a high variability in acceptability and preferences of depression treatments. Significant predictors of choice for depression treatments were administration mode depending on session duration (p < 0.001), access mode (p < 0.001) and treatment duration (p < 0.001). Significant predictors of choice for clinical trial designs were allocation type (p = 0.008) and monetary compensation (p = 0.033). Participants preferred participating in research compared to non-participation (p < 0.001). CONCLUSIONS: Accessibility and diversity of depression interventions, including psychotherapy, need to be enhanced in addiction services to ensure that all patients can receive their preferred treatment. Ensuring proper monetary compensation and comparing an intervention of interest with an active treatment might increase participation of depressed OUD patients in future clinical research initiative.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".