Research Participation in Substance Use Disorder Trials: Design and Methods of a Multi-Site Nested Qualitative Study
Bibliographic record
Abstract
Background Given the health and social harms of problematic substance use, randomized controlled trials (RCTs) are critical in developing and testing pharmacotherapies for substance use disorders. However, substance use RCTs can be challenging to conduct, considering the social and structural barriers to participating in research among people with substance use disorders (PSUD), including stigma, poverty, and criminalization—factors that can shape trial recruitment, enrollment, protocol adherence and study retention. Despite these barriers, adequate representation and participation of PSUD in RCT research is essential to assessing and developing treatments, and thus a deeper understanding of RCT participation dynamics among PSUD is needed to support clinical trial research. Methods We conducted a nested qualitative study within a Canadian, multisite, phase IV, open-label, pragmatic RCT that tested two approved opioid agonist treatments, methadone and buprenorphine/naloxone, among patients with prescription opioid use disorder. A subset of individuals ( n = 60) participating in this RCT were interviewed across four different regions in Canada at the beginning and end of their trial involvement, as well as study clinicians ( n = 16) and staff ( n = 16) operating the trial. Conclusion As a nested study within a real-world addiction medicine trial, this research offers an innovative approach to investigating the experiences, strategies, and challenges associated with RCTs among PSUD. While we acknowledge challenges related to the operations of multisite research and engaging marginalized populations in experimental research, this study has the potential to generate critical insights around the RCT experiences of PSUDs and trial staff to inform the conduct of future RCTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.131 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".