Feasibility of Testing Client Preferences for Accessing Injectable Opioid Agonist Treatment (iOAT): A Pilot Study
Bibliographic record
Abstract
Purpose: Injectable opioid agonist treatment (iOAT) is an effective treatment for opioid use disorder (OUD). To our knowledge, no research has systematically studied client preferences for accessing iOAT. Incorporating preferences could help meet the heterogenous needs of clients and make addiction care more person-centred. This paper presents a pilot study of a best-worst scaling (BWS) preference elicitation survey that aimed to assess if the survey was feasible and accessible for our population and to test that the survey could gather sound data that would suit our planned analyses. Patients and Methods: Current and former iOAT clients (n = 18) completed a BWS survey supported by an interviewer using a think-aloud approach. The survey was administered on PowerPoint, and responses and contextual field notes were recorded manually. Think-aloud audio was recorded on Audacity. Results: Clients' feedback fell into five categories: framing of the task, accessibility, conceptualization of attributes and levels, formatting, and behaviour predicting questions. Survey repetitiveness was the most consistent feedback. The data simulation showed that 100 responses should provide an adequate sample size. Conclusion: This pilot demonstrates the type of analysis that can be done with BWS in our population, suggests that such analysis is feasible, and highlights the importance of the interviewer and participant working side-by-side throughout the task.
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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) | 0.001 | 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".