Assessing support for substance use policies among the general public and policy influencers in two Canadian provinces
Bibliographic record
Abstract
BACKGROUND: Examining support for substance use policies, including those for harm reduction, among the general public and policy influencers is a fundamental step to map the current policy landscape and leverage policy opportunities. Yet, this is a knowledge gap in Canada. Our paper identifies the level of support for substance use policies in two provinces in Canada and describes how the level of support is associated with intrusiveness and sociodemographic variables. METHODS: Data came from the 2019 Chronic Disease Prevention Survey. The representative sample included members of the general public (Alberta n = 1648, Manitoba n = 1770) as well as policy influencers (Alberta n = 204, Manitoba n = 98). We measured the level of support for 22 public policies concerning substance use through a 4-point Likert-scale. The Nuffield Council on Bioethics Intervention Ladder framework was applied to assess intrusiveness. We used cumulative link models to run ordinal regressions for identification of explanatory sociodemographic variables. RESULTS: Overall, there was generally strong support for the policies assessed. The general public in Manitoba was significantly more supportive of policies than its Alberta counterpart. Some differences were found between provinces and samples. For certain substance use policies, there was stronger support among women than men and among those with higher education than those with less education. CONCLUSIONS: The results highlight areas where efforts are needed to increase support from both policy influencers and general public for adoption, implementation, and scaling of substance use policies. Socio-demographic variables related to support for substance use policies may be useful in informing strategies such as knowledge mobilization to advance the policy landscape in Western Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".