Nonmedical prescription opioid use and illegal drug use: initiation trajectory and related risks among people who use illegal drugs in Vancouver, Canada
Bibliographic record
Abstract
Abstract Objective We investigated the prevalence of and risk factors associated with initiating nonmedical prescription opioid use (NMPOU) before and after illegal drugs using data from two linked cohort studies of street youth and adults who use illegal drugs in Vancouver, Canada. All participants who attended a study visit between 2013 and 2016 were eligible for the primary analyses. Results Among 512 youth and 833 adult participants, the prevalence of NMPOU was extremely high (88% among street youth; 90% among adults), and over one-third of those who reported engaging in NMPOU had initiated NMPOU before illegal drug use (vs. transitioning from illegal drugs to NMPOU). Participants who reported either transitioning to or from NMPOU had higher risk profiles, particularly related to substance use, when compared with those who reported never engaging in NMPOU. Sub-analyses restricted to only those who engaged in NMPOU found few statistically significant differences between those who initiated NMPOU prior to illegal drugs versus those who initiated illegal drugs prior to NMPOU. Findings suggest that among people who use illegal drugs, early NMPOU trajectories do not appear to critically shape future patterns and practices.
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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".