Mapping socio-spatial contexts of drug use and service access: A community-based rapid ethnography in Northern Ontario, Canada
Bibliographic record
Abstract
Greater Sudbury, Canada, is the largest geographical city in the province of Ontario. With urban and rural characteristics, Sudbury's population density is sparse, yet it features an active downtown core. Sudbury's only supervised consumption site (SCS) closed in 2024. We examine the socio-spatial contexts of drug use and access as a governance outcome of service placement, transportation access, and policing. By mapping and identifying areas of unregulated drug use in this study, we provide qualitative maps to explore the day-to-day geographies related to unregulated drug use and service access using participatory sketch-mapping. We conducted a community-based rapid ethnography consisting of naturalistic observations and semi-structured interviews with clients and non-clients of the local SCS (n = 27 across two waves, April-August 2024). Interviews involved self-mapping the approximate areas where participants lived/stayed, accessed harm reduction services, and purchased and used unregulated drugs. Data was incorporated into ArcGIS, a geographic information system (GIS) software. To analyze spatial data and emerging patterns while ensuring participant privacy, Kernel Density Estimation was employed, which provided insights into unregulated drug activity without disclosing exact coordinates. Findings indicate a high concentration of unregulated drug activities in Sudbury's downtown core, while significant activity extended across the city, with varying visibility. Study findings exhibit both the centrality of unregulated drug use and the peripheral areas where activities are less detectable, yet still present. These findings aid us in spatially understanding unregulated drug use in a Northern setting and can inform drug strategies related to the placement of harm reduction and health services, including mobile 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".