Potential use of supervised injection services among people who inject drugs in a remote and mid-size Canadian setting
Bibliographic record
Abstract
BACKGROUND: While supervised injection services (SIS) feasibility research has been conducted in large urban centres across North America, it is unknown whether these services are acceptable among people who inject drugs (PWID) in remote, mid-size cities. We assessed willingness to use SIS and expected frequency of SIS use among PWID in Thunder Bay, a community in Northwestern, Ontario, Canada, serving people from suburban, rural and remote areas of the region. METHODS: Between June and October 2016, peer research associates administered surveys to PWID. Sociodemographic characteristics, drug use and behavioural patterns associated with willingness to use SIS and expected frequency of SIS use were estimated using bivariable and multivariable logistic regression models. Design preferences and amenities identified as important to provide alongside SIS were assessed descriptively. RESULTS: Among 200 PWID (median age, IQR: 35, 28-43; 43% female), 137 (69%) reported willingness to use SIS. In multivariable analyses, public injecting was positively associated with willingness to use (Adjusted Odds Ratio (AOR): 4.15; 95% confidence interval (CI): 2.08-8.29). Among those willing to use SIS, 87 (64%) said they would always/usually use SIS, while 48 (36%) said they would sometime/occasionally use SIS. In multivariable analyses, being female (AOR: 2.44; 95% CI: 1.06-5.65) and reporting injecting alone was positively associated with higher expected frequency of use (AOR: 2.59; 95% CI: 1.02-6.58). CONCLUSIONS: Our findings suggest that SIS could play a role in addressing the harms of injection drug use in remote and mid-sized settings particularly for those who inject in public, as well as women and those who inject alone, who report higher expected frequency of SIS use. Design preferences of local PWID, in addition to differences according to gender should be taken into consideration to maximize the uptake of SIS, alongside existing health and social service provisions available to PWID.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".