Risk creating and risk reducing: Community perceptions of supervised consumption facilities for illicit drug use
Bibliographic record
Abstract
Progressive public health authorities in high-income countries have advocated supervised consumption facilities, where people who use illicit drugs can consume them in a hygienic, supervised environment, as a way of reducing drug-related risks to both people who use drugs and communities. However, the planning of such facilities has often met with strong reactions from the local community. ‘Not in my backyard’ (NIMBY) type reactions are frequently encountered and public opinion polling is limited in its ability to provide detailed insights into the reasons why people support or oppose these facilities in Toronto and Ottawa. We explore perceptions of residents and business representatives to the proposed implementation of supervised consumption facilities, and examine their perceptions of risks from these facilities. We collected qualitative data from 2008–2010 using focus groups and interviews with 38 residents and 17 business representatives in these two large Canadian cities lacking supervised consumption facilities. We used thematic analysis to examine expressed benefits and risks regarding supervised consumption facilities amongst community members. These participants saw these facilities as potentially risk-reducing, but recognised that the facilities could also create risks for their communities. While community members accepted that facilities could have positive health effects, they expressed a level of concern regarding the risk of public nuisance associated with supervised consumption facilities that seemed unwarranted based on the existing evidence. Discussions on the risks involved in the establishment of supervised consumption facilities should move beyond a focus on the benefits to facility users, to exploring community-level benefits and risks, and integrate evidence regarding actual risk experiences from other locations. Similar approaches may apply to NIMBY concerns related to other contentious issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".