Factors associated with willingness to participate in a pharmacologic addiction treatment clinical trial among people who use drugs
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although new medications are needed to address the harms of drug addiction, rates of willingness to participate in addiction treatment trials among people who use drugs (PWUD), have not been well characterized. METHODS: One thousand twenty PWUD enrolled in two community-recruited cohorts in Vancouver, Canada, were asked whether they would be willing to participate in a pharmacologic addiction treatment trial. Logistic regression was used to identify factors independently associated with a willingness to participate. RESULTS: Among the 1,020 PWUD surveyed between June 1, 2013 and November 30, 2013, 58.3% indicated a willingness to participate. In multivariate analysis, factors independently associated with a willingness to participate in a pharmacologic addiction treatment trial included: daily heroin injection (Adjusted Odds Ratio [AOR] = 1.75; 95% Confidence Interval [CI]: 1.13 - 2.72); daily crack smoking (AOR = 1.81; 95% CI: 1.23 - 2.66); sex work involvement (AOR = 2.22; 95% CI: 1.21 - 4.06); HIV seropositivity (AOR = 1.49; 95% CI: 1.15 - 1.94); and methadone maintenance therapy participation (AOR = 1.77; 95% CI: 1.37-2.30). DISCUSSION AND CONCLUSIONS: High rates of willingness to participate in a pharmacologic addiction treatment trial were observed in this setting. Importantly, high-risk drug and sexual activities were positively associated with a willingness to participate, which may suggest a desire for new treatment interventions among PWUD engaged in high-risk behavior. SCIENTIFIC SIGNIFICANCE: These results highlight the viability of studies seeking to enroll representative samples of PWUD engaged in high-risk drug use.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".