Mapping needles, reducing harm: Findings from a geospatial, community-based needle collection and naloxone training initiative in Saskatchewan, Canada
Bibliographic record
Abstract
BACKGROUND: The opioid crisis is a major public health issue in Canada, with prairie provinces such as Saskatchewan experiencing particularly high rates of opioid-related harms. Factors contributing to this crisis include an unstable drug supply, limited access to harm reduction services, and structural challenges such as poverty and housing instability. The rise of fentanyl has further exacerbated overdose risks, particularly among people who use drugs. Harm reduction programs, such as opioid overdose education and naloxone distribution, have proven effective in reducing overdose fatalities and improving community health. While geospatial analysis has shown promise in identifying areas of high need for targeted harm reduction interventions, its integration into harm reduction strategies remains underexplored. METHODS: This study utilized data from 44 participants who completed pop-up naloxone training sessions in Regina, Saskatchewan, between August 2023 and September 2024. Data sources include geospatial information on discarded needles from the ReportNeedles.ca platform and survey responses evaluating opioid overdose and naloxone administration using a modified Opioid Overdose Knowledge Scale. Naloxone training sessions were targeted to areas with a high number of discarded needles determined by the ReportNeedles.ca platform. Geospatial analyses were conducted using ArcGIS to map needle prevalence and assess the accessibility of harm reduction services based on walk-time buffers. RESULTS: Between August 2023 and August 2024, 315 reports on ReportNeedles.ca led to the disposal of 2,836 needles. Geospatial analysis revealed clustering of discarded needles in Regina's city center, with some seasonal variation. Pop-up training sites expanded the accessibility of naloxone services, with 70% of participants reporting living within a 15-minute walk to pop-up Naloxone trainings. However, geospatial analysis revealed gaps in service accessibility specifically in suburban areas. Participants in pop-up naloxone trainings demonstrated strong knowledge of overdose recognition and naloxone administration. CONCLUSIONS: This study demonstrates potential benefit in integrating geospatial analysis with harm reduction interventions to address the opioid crisis. By identifying needle prevalence hotspots and utilizing pop-up naloxone training, service providers can improve service accessibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".