“Trying to get down here when the dealer's closer”: Place and participation in clinical trials for substance use disorders
Bibliographic record
Abstract
Place-based substance use research has emphasized how geography, institutions, and relationships shape drug use risks and meanings. Building on this work, we examine how people who use drugs navigate the spatial and institutional landscapes of clinical research by nesting a qualitative study within a multisite pragmatic trial of opioid use disorder treatment in Canada. Drawing on 111 interviews with 72 participants across four Canadian provinces, we investigate how trial engagement unfolded within the meaningful geographies of participants' everyday lives. Participants described how spatial features (e.g., proximity to policed "drugscapes"), institutional norms (e.g., surveillance, pharmacy policies), and interactions with institutional actors (e.g., stigma, support) shaped their research experiences. Sustaining participation often required the work of mobilizing social networks, applying local knowledge, and adapting daily routines. These findings highlight the equity implications of conducting research with structurally marginalized populations and demonstrate how social, spatial, and institutional conditions shape study participation, complicating assumptions of standardization in multisite trials.
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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 | 0.099 | 0.272 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".