Virtual/Mobile Overdose Response in Canada: A Social Return on Investment Analysis
Bibliographic record
Abstract
OBJECTIVES: The overdose epidemic continues to be one of the leading causes of death in North America and continues to contribute to high healthcare costs. Although harm reduction initiatives have significantly reduced the aforementioned costs, there is a dearth of evidence regarding overdose response hotlines and applications. We aim to evaluate the social return on investment from a payer perspective of one such overdose response hotline, Canada's National Overdose Response Service, and its implications for service users, service operators, the Canadian healthcare system, and program funders. METHODS: Outcome variables determined from theory of change models were developed in consultation with the aforementioned vested interest groups. Proxy values were attributed to each variable identified through values present within existing literature and databases. These values were then compared with operational costs accounting for deadweight, attribution, and displacement to determine a final social return on investment ratio. A discount rate was then applied based on the influence of risk on the outcome achieved. RESULTS: The ratio illustrating the value created for all stakeholders, resulting from the $1 592 000 investment made over 2 years, is $15.84 per single dollar invested. The value generated stems primarily from overdose prevention, mental health support, staff employment, reductions in emergency service utilization, service referrals, and volunteer well-being, which outweigh costs including operational funding, work-related stressors, compassion fatigue, and false calls. CONCLUSIONS: The results of our study demonstrate that the National Overdose Response Service provides a social value that far outweighs the costs attributed to the program's operation.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".