The Opioid Crisis: A Cost Analysis of Responding to Opioid-Related Overdoses in Calgary’s Supervised Consumption Site Versus in Public Space
Bibliographic record
Abstract
As efforts increase to transition from supervised consumption services (SCS) to recoveryfocused models, a key question emerges: how do the costs and benefits associated with operating Calgary’s SCS site, Safeworks, for overdose prevention compare to the costs associated with responding to opioid overdoses occurring in public spaces? The opioid and drug toxicity crisis continues to place pressure on public health systems, as overdoses require significant emergency service resources, and public spaces account for a large portion of opioid-related fatalities. Using a cost-benefit analysis and probability tree modelling, this capstone compares the economic costs and benefits of operating Safeworks for overdose prevention to the costs of responding to overdose events occurring in public spaces. Findings reveal that the cost per successfully reversed overdose at Safeworks is significantly lower than in a public space, saving approximately $50 for each overdose. In addition, Safeworks’ 0% mortality rate and net present value clearly demonstrate its economic value and life-saving benefits. These findings reinforce the essential role that SCS sites play in harm reduction by offering an economically responsible response to the opioid crisis, alleviating pressure on emergency services, and reducing health risks linked to delayed medical intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".