Understanding the Policy Context and Conditions Necessary for the Establishment of Supervised Consumption Sites in Canada: A Comparative Analysis of Alberta and Manitoba
Bibliographic record
Abstract
The establishment of supervised consumption sites (SCS) is one policy approach used to address Canada’s growing, national opioid epidemic. Despite the abundance of evidence which demonstrates the numerous public health benefits gained from the existence of SCS, only five of ten Canadian provinces have established SCS. Using Alberta and Manitoba as comparators, the paper seeks to explain the difference in policy outcomes. The paper begins with a brief overview of the history of harm reduction policy in Canada and establishes what the goals of the Alberta and Manitoba governments were in their respective policy approaches to respond to the opioid epidemic. Using John Kingdon’s Multiple Streams Framework, this paper compares the political and policy contexts of Alberta and Manitoba to determine what factors have contributed to the divergence in policy outcomes, whereby Alberta has established SCS while Manitoba has not. The comparative analysis reveals that the framing of the opioid epidemic as a public health matter, the alignment of the establishment of SCS with the provincial government’s values, and political will are all necessary conditions for the establishment of SCS. This paper concludes by discussing the implications of these results for the establishment of SCS in other Canadian provinces.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".