Longitudinal latent polysubstance use patterns among a cohort of people who use opioids in Vancouver, Canada
Bibliographic record
Abstract
INTRODUCTION: Polysubstance use (PSU) practices are increasing among people who use opioids (PWUO). However, several aspects of longitudinal PSU patterns among PWUO remain understudied. This study aims to identify person-centred longitudinal patterns of PSU among a cohort of PWUO. METHODS: Using longitudinal data (2005-2018) from three prospective cohort studies including people who use drugs in Vancouver, Canada, we used repeated measures latent class analysis to identify different PSU classes among PWUO. Multivariable generalised estimating equations models weighted by the respective posterior membership probabilities were applied to identify covariates of membership in different PSU classes over time. RESULTS: Overall, 2627 PWUO (median age at baseline: 36 [quartile 1-3: 25-45]) were included between 2005 and 2018. We found five distinct PSU patterns, including low/infrequent probability of regular substance use (Class 1; 30%), primarily opioid and methamphetamine use (Class 2; 22%), primarily cannabis use (Class 3; 15%), primarily opioid and crack use (Class 4; 29%) and frequent PSU (Class 5; 4%). Membership in Class 2, 4 and 5 was positively associated with several behavioural and socio-structural adversities. DISCUSSION AND CONCLUSIONS: Findings of this longitudinal study suggest PSU is the norm among PWUO and highlights the heterogeneous characteristics of PWUO. The diversities within the population of PWUO need to be recognised in addiction care and treatment as well as optimising resource allocation in the response to the overdose crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".