Profiling poly-substance use typologies in a multi-site cohort of illicit opioid and other drug users in Canada–a latent class analysis
Why this work is in the frame
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Bibliographic record
Abstract
'Poly-substance use' is increasingly prevalent among street drug user populations. The objective was to employ latent class analysis (LCA) to empirically categorize and extract potential typologies of poly-substance users within a multi-site cohort of illicit opioid and other drug users (OPICAN) in Canada, and examine potential associations with social and health indicators. Drug use patterns of 582 participants from the most recent follow-up (2005) of the cohort study–focusing on drug use prevalence indicators in the past 30 days–were empirically analyzed via LCA. These classes were further examined for associations with social and health variables using chi-square, ANOVA. Binomial logistic regression models were used to predict class membership. LCA analysis resulted in eight distinct user typologies, characterized both by the distinct relative prevalence of different substances (e.g., including: heroin, prescription opioids, benzodiazepines, cocaine, crack, alcohol, cannabis, and others) used and administration routes (e.g., injection or noninjection), the majority of which were described by the predominant use of two or more distinct substance groups (e.g., opioids and stimulants). At least two of the active poly-substance user classes were described by predominant noninjection as the primary route of administration. 'Poor or fair' health status was reported at the highest prevalence level by the class of intensive poly-substance injectors, while HCV-positive status was disproportionately low in the classes of current noninjectors. Analytical examination of poly-substance use patterns is a distinct challenge for meaningful drug use monitoring, also providing important evidence for targeted prevention and treatment interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it