Willingness to use an in‐hospital supervised inhalation room among people who smoke crack cocaine in Vancouver, Canada
Bibliographic record
Abstract
INTRODUCTION AND AIMS: People who use illicit drugs (PWUD) often engage in drug use during hospitalisation. Adverse outcomes may arise from efforts to conceal inpatient drug use, especially in hospital settings that rely on abstinence-based policies. Harm reduction interventions, including supervised drug consumption services, have not been well studied in hospital settings. This study examines factors associated with willingness to use an in-hospital supervised inhalation room (SIR) among people who smoke crack cocaine in Vancouver, Canada. DESIGN AND METHODS: Cross-sectional data from two open prospective cohorts of PWUD involving people who smoke crack cocaine were collected between June 2013 and May 2014. Multivariable logistic regression analyses were used to identify factors associated with willingness to use an in-hospital SIR. RESULTS: Among 539 participants, 320 (59.4%) reported willingness to use an in-hospital SIR. Independent factors positively associated with willingness included: ever used drugs in hospital [adjusted odds ratio (AOR) = 1.89], and daily non-injection crack use (AOR = 1.63). Difficulty accessing new crack pipes (AOR = 0.51) was negatively associated with willingness (all P < 0.05). The most commonly reported reasons for willingness were to: remain in hospital (50.6%), reduce drug-related risks (25.6%) and reduce the stress of hospital discharge for using drugs (24.7%). DISCUSSION AND CONCLUSIONS: A high proportion of people who smoke crack cocaine reported willingness to use an in-hospital SIR, and those willing were more likely to report heavy drug use and previous in-hospital use. These findings highlight the potential utility of SIRs to complement existing in-hospital services for PWUD.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".