Frequency of supervised consumption service use and acute care utilization in people who inject drugs
Bibliographic record
Abstract
BACKGROUND: Supervised consumption service (SCS) use among people who inject drugs may reduce acute care utilization; however, prior studies have been limited by self-reported outcomes and dichotomous exposures. METHODS: We conducted a prospective cohort study using linked questionnaire and health administrative data among people who inject drugs in Toronto, Canada (2018-2020). Baseline SCS use frequency was defined by a participant's self-reported proportion of injections performed at an SCS over the past six months: "all/most" (≥75 %), "some" (26-74 %), "few" (1-25 %), or "none" (0 %). Outcomes measured over the following six months included: emergency department (ED) visits; hospitalizations; ED visits or hospitalizations for opioid-related overdose; and hospitalizations for injection-related infections. The relative effects of varying SCS use levels on study outcomes were estimated using inverse-probability-weighted negative binomial regression models. RESULTS: Of 467 participants, 25.5 %, 30.4 %, 28.7 %, and 15.4 % respectively reported "all/most", "some", "few", and "none" levels of SCS use at baseline. SCS use frequency was not significantly associated with ED visits, hospitalizations, or hospitalizations for injection-related infections. Participants reporting "some" SCS use had a higher rate of ED visits or hospitalizations for opioid-related overdose (versus "few"; rate ratio=2.30, 95 % confidence interval=1.15-4.61). CONCLUSIONS: SCS use had little impact on objectively measured acute care utilization, which was high overall. Although preventing overdose mortality is the primary goal of SCS in Canada, resourcing sites to support their clients' acute healthcare needs may help build a continuum of care for people experiencing marginalization who inject drugs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".