Implementing Canada’s first national virtual phone based overdose prevention service: lessons learned from creating the National Overdose Response Service (NORS)
Bibliographic record
Abstract
The opioid epidemic remains one of the largest public health crises in North America to date. While there have been many diverse strategies developed to reduce the harms associated with substance use, these are primarily concentrated within a few large urban centers. As a result, there have been increased calls for equitable access to harm reduction services for those who cannot or choose not to access in-person harm reduction services. In December 2020, Canada's National Overdose Response Service (NORS) a telephone based overdose response hotline and virtual supervised consumption service, was established in collaboration with various agencies and people with lived and living experience of substance use (PWLLE) across Canada to expand access to harm reduction services using novel Opioid Response Technology. In this manuscript we explore the lessons learned from the establishment and continued operation of the service exploring topics related to the initial establishment of the service, securing a phone line, routing technology, EMS dispatch solutions, peer and volunteer recruitment, legal and ethical support, policy and procedure development, securing funding, and marketing. Furthermore, we detail how this service has grown and changed in response to the various needs of service users.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".