Latent polydrug use patterns and the provision of injection initiation assistance among people who inject drugs in three North American settings
Bibliographic record
Abstract
INTRODUCTION: We sought to identify latent profiles of polysubstance use patterns among people who inject drugs in three distinct North American settings, and then determine whether profile membership was associated with providing injection initiation assistance to injection-naïve persons. METHODS: Cross-sectional data from three linked cohorts in Vancouver, Canada; Tijuana, Mexico; and San Diego, USA were used to conduct separate latent profile analyses based on recent (i.e., past 6 months) injection and non-injection drug use frequency. We then assessed the association between polysubstance use patterns and recent injection initiation assistance provision using logistic regression analyses. RESULTS: A 6-class model for Vancouver participants, a 4-class model for Tijuana participants and a 4-class model for San Diego participants were selected based on statistical indices of fit and interpretability. In all settings, at least one profile included high-frequency polysubstance use of crystal methamphetamine and heroin. In Vancouver, several profiles were associated with a greater likelihood of providing recent injection initiation assistance compared to the referent profile (low-frequency use of all drugs) in unadjusted and adjusted analyses, however, the inclusion of latent profile membership in the multivariable model did not significantly improve model fit. DISCUSSION AND CONCLUSIONS: We identified commonalities and differences in polysubstance use patterns among people who inject drugs in three settings disproportionately impacted by injection drug use. Our results also suggest that other factors may be of greater priority when tailoring interventions to reduce the incidence of injection initiation. These findings can aid in efforts to identify and support specific higher-risk subpopulations of people who inject drugs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".