Feasibility, acceptability, concerns, and challenges of implementing supervised injection services at a specialty HIV hospital in Toronto, Canada: perspectives of people living with HIV
Bibliographic record
Abstract
Abstract Background Substance use significantly impacts health and healthcare of people living with HIV/AIDS (PLHIV), especially their ability to remain in hospital following admission. Supervised injection services (SIS) reduce overdoses and drug-related harms, but are not often provided within hospitals/outpatient programs. Leading us to question, what are PLHIV’s perceptions of hospital-based SIS? Methods This mixed-methods study explored feasibility and acceptability of implementing SIS at Casey House, a Toronto-based specialty HIV hospital, from the perspective of its in/outpatient clients. We conducted a survey, examining clients’ (n = 92) demand for, and acceptability of, hospital-based SIS. Following this, we hosted two focus groups (n = 14) and one-on-one interviews (n = 8) with clients which explored benefits/drawbacks of in-hospital SIS, wherein participants experienced guided tours of a demonstration SIS space and/or presentations of evidence about impacts of SIS. Data were analysed using descriptive statistics and thematic analysis. Results Among survey participants, 76.1% (n = 70) identified as cis-male and over half (n = 49;54.4%) had been a hospital client for 2 years or less. Nearly half (48.8%) knew about clients injecting in/near Casey House, while 23.6% witnessed it. Survey participants were more supportive of SIS for inpatients (76.1%) than for outpatients (68.5%); most (74.7%) reported SIS implementation would not impact their level of service use at Casey House, while some predicted coming more often (16.1%) and others less often (9.2%). Most focus group/interview participants, believed SIS would enhance safety by reducing health harms (e.g. overdose), increasing transparency between clients and clinicians about substance use, and helping retain clients in care. Debate arose about who (e.g., in/outpatients vs. non-clients) should have access to hospital-based SIS and how implementation may shift organizational priorities/resources away from services not specific to drug use. Conclusions Our data showed widespread support of, and need for, hospital-based SIS among client stakeholders; however, attempts to reduce negative impacts on non-drug using clients need to be considered in the balance of implementation plans. Given the increased risks of morbidity and mortality for PLHIV who inject drugs as well as the problems in retaining them in care in a hospital setting, SIS is a key component of improving care for this marginalized group.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".