Willingness to access an in-hospital supervised injection facility among hospitalized people who use illicit drugs
Bibliographic record
Abstract
Background Despite the reliance on abstinence-based drug policies within hospital settings, illicit drug use is common among hospitalized patients with severe drug addiction. Hospitalized people who use illicit drugs (PWUD) have been known to resort to high-risk behaviours to conceal their drug use from healthcare providers. Novel interventions with potential to reduce high-risk behaviours among PWUD in hospital settings have not been well studied. Objective The objective of the study was to examine factors associated with willingness to access an in-hospital supervised injection facility (SIF). Design Data were derived from participants enrolled in two Canadian prospective cohort studies involving PWUD between June 2013 and November 2013. A cross-sectional study surveying various socio-demographic characteristics, drug use patterns and experiences was conducted. Setting Vancouver, Canada Measurements Bivariable and multivariable logistic regression analyses were used to explore factors significantly associated with willingness to access an in-hospital SIF. Results Among 732 participants, 499 (68.2%) would be willing to access an in-hospital SIF. In multivariable analyses, factors positively and significantly associated with willingness to access an in-hospital SIF included: daily heroin injection (adjusted odds ratio [AOR] = 1.90; 95% confidence interval [CI]: 1.20 – 3.11); having used illicit drugs in hospital (AOR = 1.63; 95%CI: 1.18 – 2.26); and having recently used a SIF (AOR = 1.53; 95%CI: 1.10 – 2.15). Conclusions Our findings highlight the potential of in-hospital SIFs to complement existing harm reduction programs that serve PWUD. Moreover, an in-hospital SIF may minimize the harms associated with high-risk illicit drug use in hospital.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".