Chronic disease prevention policy in British Columbia and Ontario in light of public health renewal: a comparative policy analysis
Bibliographic record
Abstract
BACKGROUND: Public health strategies that focus on legislative and policy change involving chronic disease risk factors such as unhealthy diet and physical inactivity have the potential to prevent chronic diseases and improve quality of life as a whole. However, many public health policies introduced as part of public health reform have not yet been analyzed, such as in British Columbia and Ontario. The purpose of this paper is to present the results of a descriptive, comparative analysis of public health policies related to the Healthy Living Core Program in British Columbia and Chronic Disease Prevention Standard in Ontario that are intended to prevent a range of chronic diseases by promoting healthy eating and physical activity, among other things. METHODS: Policy documents were found through Internet search engines and Ministry websites, at the guidance of policy experts. These included government documents as well as documents from non-governmental organizations that were implementing policies and programs at a provincial level. Documents (n = 31) were then analysed using thematic content analysis to classify, describe and compare policies in a systematic fashion, using the software NVivo. RESULTS: Three main categories emerged from the analysis of documents: 1) goals for chronic disease prevention in British Columbia and Ontario, 2) components of chronic disease prevention policies, and 3) expected outputs of chronic disease prevention interventions. Although there were many similarities between the two provinces, they differed somewhat in terms of their approach to issues such as evidence, equity, and policy components. Some expected outputs were adoption of healthy behaviours, use of information, healthy environments and increased public awareness. CONCLUSIONS: The two provincial policies present different approaches to support the implementation of related programs. Differences may be related to contextual factors such as program delivery structures and different philosophical approaches underlying the two frameworks. These differences and possible explanations for them are important to understand because they serve to contextualize the differences in health outcomes across the two provinces that might eventually be observed. This analysis informs future public health policy directions as the two provinces can learn from each other.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".