Interest in Getting Help to Reduce or Stop Substance Use Among Syringe Exchange Clients Who Use Opioids
Bibliographic record
Abstract
OBJECTIVES: Opioid use is a growing problem in the United States. Despite existence of effective treatments (eg, opioid agonist medication), most people with opioid use disorder do not receive treatment. Increasing treatment receipt is an essential component of the response to the opioid crisis. We examined factors associated with interest in getting help to reduce or stop substance use among syringe exchange program (SEP) clients who reported using opioids. METHODS: Surveys were administered at 17 SEPs across Washington State during 2015; 436 respondents who reported recent opioid use and not receiving current treatment were eligible for this analysis. Multivariable logistic regression was conducted to examine factors associated with being somewhat or very interested in getting help, including sociodemographic characteristics, substance use behaviors and outcomes, and use of health care services. RESULTS: Most participants reported interest in getting help (77.5%). Factors positively associated with interest included female gender (adjusted odds ratio [AOR] = 1.79; 95% confidence interval [CI]: 1.03, 3.11), having an abscess (AOR = 1.87; 95% CI: 1.02, 3.40), and having received treatment (AOR = 4.83; 95% CI: 1.77, 13.14) or other services (AOR = 3.01; 95% CI: 1.06, 8.54) in the past year. Recent methamphetamine use was negatively associated with interest in getting help (AOR = 0.49; 95% CI: 0.26, 0.91). CONCLUSIONS: In this survey of SEP clients, interest in getting help to reduce or stop substance use was prevalent and varied across subpopulations of persons using opioids. Findings point to SEPs as an important venue for treatment engagement, and suggest subgroups who may be targeted for engagement interventions.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".