Assessing support for mental health policies among policy influencers and the general public in Alberta and Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: There is a need to improve mental health policy in Canada to address the growing population burden of mental illness. Understanding support for policy options is critical for advocacy efforts to improve mental health policy. Our purpose was to describe support for population-level healthy public policies to improve mental health among policy influencers and the general public in Alberta and Manitoba; and, identify associations between levels of support and sociodemographic variables and relative to the Nuffield Bioethics Intervention Ladder framework. METHODS: We used data from the 2019 Chronic Disease Prevention Survey, which recruited a representative sample of the general public in Alberta (n = 1792) and Manitoba (n = 1909) and policy influencers in each province (Alberta n = 291, Manitoba n = 129). Level of support was described for 16 policy options using a Likert-style scale for mental health policy options by province, sample type, and sociodemographic variables using ordinal regression modelling. Policy options were coded using the Nuffield Council on Bioethics Intervention Ladder to classify support for policy options by level of intrusiveness. RESULTS: Policy options were categorized as 'Provide Information' and 'Enable Choice' according to the Nuffield Intervention Ladder. There was high support for all policy options, and few differences between samples or provinces. Strong support was more common among women and among those who were more politically left (versus center). Immigrants were more likely to strongly support most of the policies. Those who were politically right leaning (versus center) were less likely to support any of the mental health policies. Mental health status, education, and Indigenous identity were also associated with support for some policy options. CONCLUSIONS: There is strong support for mental health policy in Western Canada. Results demonstrate a gap between support and implementation of mental health policy and provide evidence for advocates and policy makers looking to improve the policy landscape in 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".