Service delivery models for injectable opioid agonist treatment in Canada: 2 sequential environmental scans
Bibliographic record
Abstract
BACKGROUND: Injectable opioid agonist treatment (iOAT) is an emerging evidence-based option in the continuum of care for opioid use disorder in parts of Canada. Our study objective was to identify and describe iOAT programs operating during the ongoing opioid overdose crisis. METHODS: We conducted 2 sequential environmental scans. Programs were eligible to participate if they were in operation as of Sept. 1, 2018, and Mar. 1, 2019. Information was collected over 2-3 months for each scan (September-October 2018, March-May 2019). Programs that participated in the first scan and newly established programs were invited to participate in the second scan. The scans included questions about location, service delivery model, clinical and operational characteristics, numbers and demographic characteristics of clients, and program barriers and facilitators. Descriptive analysis was performed. RESULTS: We identified 14 unique programs across the 2 scans. Eleven programs located in urban centres in British Columbia and Ontario participated in the first scan. At the time of the second scan, 2 of these programs were on hold and 2 of 3 newly established programs were in Alberta. The total capacity of all participating programs was 420 clients at most. Four service delivery models were identified; iOAT was most commonly integrated within existing health and social services. All programs offered hydromorphone, and 1 program also offered diacetylmorphine. In the first scan, 73% of clients (133/183) were male; the mean age of clients was 47 years. Limited capacity, pharmacy operations and lack of diacetylmorphine access were among the most frequently reported barriers. The most commonly reported facilitators included client-centred care, client relationships and access to other health and social support. INTERPRETATION: Evidence indicates that iOAT can be successfully implemented using diverse service delivery models. Future work should facilitate scale-up of this evidence-based treatment where gaps persist in high-risk communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".