The Ontario Integrated Supervised Injection Services Cohort Study of People Who Inject Drugs in Toronto, Canada (OiSIS-Toronto): Cohort Profile
Bibliographic record
Abstract
The Ontario Integrated Supervised Injection Services cohort in Toronto, Canada (OiSIS-Toronto) is an open prospective cohort of people who inject drugs (PWID). OiSIS-Toronto was established to evaluate the impacts of supervised consumption services (SCS) integrated within three community health agencies on health status and service use. The cohort includes PWID who do and do not use SCS, recruited via self-referral, snowball sampling, and community/street outreach. From 5 November 2018 to 19 March 2020, we enrolled 701 eligible PWID aged 18+ who lived in Toronto. Participants complete interviewer-administered questionnaires at baseline and semi-annually thereafter and are asked to consent to linkages with provincial healthcare administrative databases (90.2% consented; of whom 82.4% were successfully linked) and SCS client databases. At baseline, 86.5% of participants (64.0% cisgender men, median ([IQR] age= 39 [33-49]) had used SCS in the previous 6 months, of whom most (69.7%) used SCS for <75% of their injections. A majority (56.8%) injected daily, and approximately half (48.0%) reported fentanyl as their most frequently injected drug. As of 23 April 2021, 291 (41.5%) participants had returned for follow-up. Administrative and self-report data are being used to (1) evaluate the impact of integrated SCS on healthcare use, uptake of community health agency services, and health outcomes; (2) identify barriers and facilitators to SCS use; and (3) identify potential enhancements to SCS delivery. Nested sub-studies include evaluation of "safer opioid supply" programs and impacts of COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".