‘It's our safe sanctuary’: Experiences of using an unsanctioned overdose prevention site in Toronto, Ontario
Bibliographic record
Abstract
BACKGROUND: Overdose prevention sites (OPSs) are spaces where people can consume drugs under the supervision of trained volunteers or staff and receive help in the event of an overdose. Unsanctioned OPSs are a grassroots response to the current opioid crisis in Canada. METHODS: We used rapid evaluation methods to study the experiences of 30 individuals accessing the smoking and injection services at the first unsanctioned OPS in Toronto, Ontario using semi-structured interviews. Data were analyzed using an applied thematic analysis approach to identify emergent themes related to service user experiences, characteristics of the risk environment, and recommended changes to the service model. RESULTS: The OPS represented a safe sanctuary and brought a sense of belonging to a community that often experiences discrimination. Valued aspects included: shelter; protection from violence; safety from overdoses; free equipment; information about health and social services; food and beverages; and socializing and connecting with others. Integrating peer workers in the design and delivery of services encouraged service users to visit the site. The OPS changed the risk environment by: providing access to the first supervised smoking service in Toronto; having few explicit rules and a communal approach to making new rules; allowing assisted injection, and negotiating with police to allow people to access the site with minimal contact. Service users noted the need to ensure a safe space for women and recommended extended hours of operation and moving to a more permanent space with heat and lighting for both smoking and injecting drugs. CONCLUSION: The unsanctioned OPS in Toronto served an important role in defining new, community-led, flexible responses to opioid overdose-related deaths at a time of markedly increasing mortality. Providing harm reduction services in diverse settings and expanding services to include smoking and assisted injection may increase access for marginalized people who use 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".