Assessing the public health impact of cannabis legalization in Canada: core outcome indicators towards an ‘index’ for monitoring and evaluation
Bibliographic record
Abstract
The legalization of non-medical cannabis use and supply is impending in Canada. This constitutes a major policy change with the declared objective of improving public health outcomes, which requires rigorous monitoring and evaluation. While numerous different aspects associated with legalization will be examined, a focused perspective is required for effective policy evaluation purposes. To these ends, we have identified a set of 10 core indicators associated with cannabis-related risk/harm outcomes-based on current best evidence-that are expected to measure the primary impacts of legalization on public health outcomes. We briefly review these indicators, and their respective data availability in Canada. As ideally an integrated outcome assessment of cannabis legalization's impact on public health will be available, we further propose options to merge the individual indicators into an integrated, weighted 'index', considering their expected relative impact for public health. One possible approach to undertake this is 'multi-criteria decision analysis' as a method to weight the relative indicator impact on public health; alternative approaches are proposed. The integrated 'public health index' for cannabis legalization will allow for scientifically comprehensive, while focused, monitoring and evaluation of the effects of legalization in Canada for the benefits of science and evidence-based policy alike.
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 | 0.037 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".