‘This could be my last chance’: Therapeutic optimism in a randomised controlled trial for substance use disorders
Bibliographic record
Abstract
In randomised controlled trials (RCTs), 'therapeutic optimism' describes a participant's belief they will benefit from the study treatment, despite the express goal of RCTs to test unknown aspects of interventions. Harbouring such expectations may interfere with RCT participation experiences, particularly among marginalised populations, such as people with substance use disorders (PSUD) who may experience social and structural barriers to participation that also increase their vulnerability to therapeutic optimism. However, little research explores therapeutic optimism within substance use trials. Thus, we conducted a nested qualitative study within an RCT testing a treatment for alcohol and opioid use disorders in HIV clinics. Using interviews with 22 participants in Vancouver, Canada, analysis revealed themes relevant to therapeutic optimism, that were specifically linked to intrinsic (e.g. health-related) or extrinsic motivations (e.g. stipend). First, compared to extrinsically motivated participants, intrinsically motivated participants held high expectations for the trial and attributed greater agency to the study medication. Second, intrinsically motivated participants expressing therapeutic optimism anticipated marked changes in their lives from the study/medication. Finally, some participants predicted the treatment would solve substance-related issues in their communities. These findings highlight the interplay between therapeutic optimism and complex interpretations of RCT objectives among PSUD.
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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 | 0.523 | 0.585 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".