Illicit drug use in acute care settings
Bibliographic record
Abstract
INTRODUCTION AND AIMS: While persons with addiction are often hospitalised, hospitals typically employ abstinence-based policies specific to illicit drug use. Although illicit drug use is known to occur within hospitals, this problem has not been well characterised. Therefore, we sought to investigate the prevalence of and factors associated with having ever used drugs in hospital among people who use drugs in Vancouver, Canada. DESIGN AND METHODS: Data were derived from prospective cohort studies of people who use drugs between December 2012 and May 2013. Multivariable logistic regression was used to identify demographic and behavioural factors associated with having ever used illicit drugs in hospital. RESULTS: Among 1028 participants who had experienced ≥1 hospitalisation, 43.9% reported having ever used drugs while hospitalised. In multivariable analyses, factors positively associated with having ever used drugs in hospital included daily cocaine injection and daily crack non-injection (both P < 0.05). Factors negatively associated with the outcome included older age and male gender (both P < 0.05). The most common reasons for drug use in hospital were 'wanting to use' and 'being in withdrawal'. Drugs were most commonly used in patient washrooms. DISCUSSION AND CONCLUSIONS: Our findings demonstrate that an abstinence-based approach to drug use in hospitals may be ineffective at prohibiting drug consumption. High-risk drug use behaviours arising from ongoing drug use may pose risks for further harm and illness. Efforts to minimise the harms associated with using drugs in hospital are urgently needed. [Grewal HK, Ti L, Hayashi K, Dobrer S, Wood E, Kerr T. Illicit drug use in acute care settings. Drug Alcohol Rev 2015;34:499-502].
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".