The influence of poly-drug use patterns on the association between opioid agonist treatment engagement and injecting initiation assistance
Bibliographic record
Abstract
BACKGROUND: Evidence suggests people who inject drugs (PWID) prescribed opioid agonist treatment (OAT) are less likely to provide injection drug use (IDU) initiation assistance. We investigated the association between OAT engagement and providing IDU initiation assistance across poly-drug use practices in Vancouver, Canada. METHODS: Preventing Injecting by Modifying Existing Responses (PRIMER) is a prospective study seeking to identify structural interventions that reduce IDU initiation. We employed data from linked cohorts of PWID in Vancouver and extended the findings of a latent profile analysis (LPA). Multivariable logistic regression models were performed separately for the six poly-drug use LPA classes. The outcome was recently assisting others in IDU initiation; the independent variable was recent OAT engagement. RESULTS: Among participants (n = 1218), 85 (7.0%) reported recently providing injection initiation assistance. When adjusting for age and sex, OAT engagement among those who reported a combination of high-frequency heroin and methamphetamine IDU and low-to-moderate-frequency prescription opioid IDU and methamphetamine non-injection drug use (NIDU) was associated with lower odds of IDU initiation assistance provision (Adjusted Odds Ratio [AOR]: 0.18, 95% CI: 0.05-0.63, P = 0.008). Significant associations were not detected among other LPA classes. CONCLUSIONS: Our findings extend evidence suggesting that OAT may provide a population-level protective effect on the incidence of IDU initiation and suggest that this effect may be specific among PWID who engage in high-frequency methamphetamine and opioid use. Future research should seek to longitudinally investigate potential causal pathways explaining the association between OAT and initiation assistance provision among PWID to develop tailored intervention efforts.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".