Implementation contexts and the impact of policing on access to supervised consumption services in Toronto, Canada: a qualitative comparative analysis
Bibliographic record
Abstract
BACKGROUND: Supervised consumption services (SCS) are being implemented across Canada in response to a variety of drug-related harms. We explored the implementation context of newly established SCS in Toronto and the role of policing in shaping program access by people who inject drugs (PWID). METHODS: We conducted one-to-one qualitative semi-structured interviews with 24 PWID. Participants were purposively recruited. Ethnographic observations were conducted at each of the study sites as well as in their respective neighbourhoods. Relevant policy documents were also reviewed. RESULTS: Policing was overwhelmingly discussed by participants from both SCS sites. However, participant responses varied depending on the site in question. Subthemes from participant responses on policing at site #1 described neighbourhood police presence and fears of police harassment and drug arrests before, during, or after accessing SCS. Conversely, subthemes from participant responses on policing at site #2 described immunity and protection from police while using the SCS, as well as a lack of police presence or fears of police harassment and arrests. These differences in implementation contexts were largely shaped by differences in local neighbourhoods and drug scenes. Police policies highlighted federal laws protecting PWID within SCS, but also the exercise of discretion when applying the rule of law outside of these settings. CONCLUSIONS: Participants' perspectives on, and experiences with, policing as they relate to accessing SCS were shaped by the implementation contexts of each SCS site and how neighbourhoods, drug scenes, and differences in policing practices affected service use. Our findings also demonstrate the disconnect between the goals of policing and those of SCS. Until larger structural barriers are addressed (e.g. criminalization), future SCS programming should consider the impact of policing on the SCS implementation context to improve client experience with, and access to, SCS.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".