Mobile supervised consumption services in Rural British Columbia: lessons learned
Bibliographic record
Abstract
BACKGROUND: In 2016, a public health emergency was declared in British Columbia due to an unprecedented number of illicit drug overdose deaths. Injection drug use was implicated in approximately one third of overdose deaths. An innovative delivery model using mobile supervised consumption services (SCS) was piloted in a rural health authority in BC with the goals of preventing overdose deaths, reducing public drug use, and connecting clients to health services. METHODS: Two mobile SCS created from retrofitted recreational vehicles were used to serve the populations of two mid-sized cities: Kelowna and Kamloops. Service utilization was tracked, and surveys and interviews were completed to capture clients', service providers', and community stakeholders' attitudes towards the mobile SCS. RESULTS: Over 90% of surveyed clients reported positive experiences in terms of access to services and physical safety of the mobile SCS. However, hours of operation met the needs of less than half of clients. Service providers were generally dissatisfied with the size of the space on the mobile SCS, noting constraints in the ability to respond to overdose events and meaningfully engage with clients in private conversations. Additional challenges included frequent operational interruptions as well as poor temperature control inside the mobile units. Winter weather conditions resulted in cancelled shifts and disrupted services. Among community members, there was variable support of the mobile SCS. CONCLUSIONS: Overall, the mobile SCS were a viable alternative to a permanent site but presented many challenges that undermined the continuity and quality of the service. A mobile site may be best suited to temporarily provide services while bridging towards a permanent location. A needs assessment should guide the stop locations, hours of operation, and scope of services provided. Finally, the importance of community engagement for successful implementation should not be overlooked.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".