Non‐medical prescription opioid use and in‐hospital illicit drug use among people who use drugs
Bibliographic record
Abstract
INTRODUCTION: Illicit drug use while admitted to hospital is common amongst people who use drugs. Furthermore, non-medical prescription opioid use (NMPOU) is increasingly being used by this population. This study was undertaken to investigate the relationship between NMPOU and having ever reported using illicit drugs in the hospital. METHODS: This study was a cross-sectional study design based on data derived from participants enrolled in three Canadian prospective cohort studies between December 2011 and November 2016. Using bivariable and multivariable logistic regression analyses, we examined the relationship between NMPOU and having ever reported illicit drug use in the hospital. RESULTS: Among the 1865 participants (951 male, 471 female) enrolled in the studies, 1422 (76.25%) met the inclusion criteria of having ever been hospitalised. Of these, 436 (30.7%) had used illicit drugs while in the hospital. In multivariable analyses, after adjusting for various confounders, we found a positive relationship between the percentage of reporting at least daily NMPOU in the past 6 months during the cohort study period and illicit drug use in the hospital (adjusted odds ratio 3.42; 95% confidence interval 1.46-8.02). DISCUSSION AND CONCLUSIONS: Among our sample, more persistent NMPOU was positively associated with having reported in-hospital illicit drug use. Our findings point to the need for better identification and management of opioid use disorder in acute care settings to reduce in-hospital illicit drug use, and to offer evidence-based medical treatments to achieve the most optimal outcomes for patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".