The use of knowledge translation and legal proceedings to support evidence-based drug policy in Canada : opportunities and ongoing challenges
Bibliographic record
Abstract
There is growing recognition, particularly in the areas of illicit drug policy and HIV prevention, that policy-makers are in many instances implementing suboptimal programs and services because they are not basing their decisions on the best available scientific evidence. One notable example where a policy-making body has failed to use scientific evidence to inform policy is the Canadian federal government's opposition to Vancouver's supervised injection facility despite a large body of scientific evidence indicating that the program is associated with a range of health and social benefits. Two of the key strategies that have been used to try to shift drug policy toward an evidence-based approach and maintain the operation of this evidence-based health facility are knowledge translation and legal actions. We provide an overview of these two strategies and hope it will offer lessons for the implementation of evidence-based approaches in other controversial areas of public policy.
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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 | 0.338 | 0.481 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.030 | 0.054 |
| Scholarly communication | 0.045 | 0.026 |
| Open science | 0.019 | 0.030 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".