Examining opioid agonist treatment (OAT) site operations and early signals of change in the first year of British Columbia’s drug decriminalization policy: Insights from a provincial survey
Bibliographic record
Abstract
OBJECTIVE: On January 31, 2023, the province of British Columbia (BC) introduced a 3-year drug decriminalization initiative, with a goal of increasing access, engagement, and retention in drug use treatment. There is limited information on the operational characteristics of opioid agonist treatment (OAT) sites in BC. These data are required to monitor the impacts of decriminalization on these outcomes. This study sought to characterize OAT service operations and examine any preliminary operational changes following decriminalization, from the perspectives of OAT site staff. METHODS: Between March and April 2024, a cross-sectional, online self-report survey was distributed to OAT sites across BC, completed by site representatives. Questions focused on OAT service operations, including service capacity, treatment retention, and clientele demographics, as well as potential changes to service operations due to decriminalization. Data were analyzed descriptively. RESULTS: A total of 28 OAT sites from across BC completed the survey. Findings suggest that decriminalization has had limited impacts on OAT site operations within the first year of the policy's implementation. However, several sites reported early signals of change related to client socio-demographics, including seeing more male and younger clients, as well as an increase in demand on their staff and resources. CONCLUSION: Despite minimal changes to OAT site operations within the first year of BC's decriminalization policy, findings suggest the need for increased staff training on decriminalization and continued investments into OAT to better support the anticipated demand on services if decriminalization is to successfully reach its goal of improving access to treatment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".