Association of drug overdoses and user characteristics of Canada’s national mobile/virtual overdose response hotline: the National Overdose Response Service (NORS)
Bibliographic record
Abstract
BACKGROUND: Several novel overdose response technology interventions, also known as mobile overdose response services (MORS), have emerged as adjunct measures to reduce the harms associated with the drug poisoning epidemic. This retrospective observational study aims to identify the characteristics and outcomes of individuals utilizing one such service, the National Overdose Response Service (NORS). METHODS: A retrospective analysis was conducted using NORS call logs from December 2020 to April 2023 imputed by operators. A variety of variables were examined including demographics, substance use and route, location, and call outcomes. Odds ratios and 95% confidence intervals were calculated around variables of interest to test the association between key indicators and drug poisonings. RESULTS: Of the 6528 completed calls on the line, 3994 (61.2%) were for supervised drug consumption, 1703 (26.1%) were for mental health support, 354 (5.42%) were for harm reduction education or resources, and 477 (7.31%) were for other purposes. Overall, there were 77 (1.18%) overdose events requiring a physical/ in-person intervention. Of the total calls, 3235 (49.5%) were from women, and 1070 (16.3%) were from people who identified as gender diverse. Calls mostly originated from urban locations (n = 5796, 88.7%) and the province of Ontario (n = 4137, 63.3%). Odds ratios indicate that using opioids (OR 6.72, CI 95% 3.69-13.52), opioids in combination with methamphetamine (OR 9.70, CI 95% 3.24-23.06), multiple consumption routes (OR 6.54, CI 95% 2.46-14.37), and calls occurring in British Columbia (B.C) (OR 3.55, CI 95% 1.46-7.33) had a significantly higher likelihood of a drug poisoning. No deaths were recorded and only 3 false callouts had occurred. The overall drug poisoning event incidence to phone calls was 1.2%. CONCLUSION: NORS presents a complimentary opportunity to access harm reduction services for individuals that prefer to use alone or face barriers to accessing in-person supervised consumption services especially gender minorities with high-risk use patterns.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".