Challenges, Skepticism, and Recommendations from Police about Working in Collaboration with Supervised Consumption Services
Bibliographic record
Abstract
Background The operation of supervised consumption services (SCS), programs that offer supervised locations for the consumption of drugs such as heroin, other opioids, cocaine, other stimulants, and other drugs, is contingent on cooperative relationships with police. In this manuscript, we focus on an under studied topic: how active duty police officers understand their role with respect to SCS, and their recommendations for improving relationships with these programs. Methods: During 2018, we audio-recorded and transcribed focus group discussions conducted in four police divisions with 26 police constables and 3 officers of other ranks in Toronto, Canada. Officers were asked about their roles relative to SCS, perceived training needs, and how to build cooperative relationships with SCS. We followed an iterative thematic approach to analyze the data. Results: Participants in three divisions were unclear about their roles, duties, and policy governing policing of SCS. Participants in the fourth division had received instructions from the division commander to observe a “distance and discretion policy”. However, these participants believed that developing familiarity with the new SCS, instead of keeping a distance, would be a more effective approach to building working relationships with staff, clients, and the local community. There was nearly unanimous skepticism about the willingness of SCS workers to work cooperatively with the police. These perceptions were commonly linked to previous negative experiences with harm reduction workers and, in some cases, staff at the new SCS. Several participants also linked problems in communication to fundamental differences between the philosophies or worldviews of SCS staff versus those of police. Conclusions: Building collaborative SIS-police relationships can be assisted by demystifying the operation of SCS for police, articulating policies, and working toward mutual respect.
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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 | 0.067 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.034 | 0.025 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".