Critical Success Factors for Promoting Healthy Food Environments and Healthy Eating Through Local Policy Changes: Learning From Canada
Bibliographic record
Abstract
Background and context: Policies implemented at the local level can create healthier environments that enable individuals to engage in healthier, cancer preventive behaviors - such as healthy eating. Policies support cancer preventive behaviors in a sustainable and often cost-effective manner. Many theoretical frameworks exist to describe the policy process; however in practice, policy development is often considered a complex and unfamiliar mechanism to the cancer prevention and health promotion community. Aim: To identify and better understand the critical success factors underlying cancer prevention policy success, the Canadian Partnership Against Cancer analyzed the policy outcomes - focused on food environments and healthy eating - from their pan-Canadian funding initiative Coalitions Linking Action and Science for Prevention (CLASP). Strategy/Tactics: Four projects funded through the CLASP initiative, from 2009 to 2016, have yielded 260 policy outcomes related to improving food environments and healthy eating. The policy changes were the result of evidence-based interventions implemented at the local level (i.e., municipalities, schools/child care, and workplaces). Program/Policy process: Over 220 knowledge products and evaluation documents were reviewed to identify food environment and healthy eating policy outcomes and key lessons learned. The policy outcomes were analyzed and categorized according to: a) implementation setting (municipality, school/child care, workplace); and b) policy lever addressed. Policy lever categories were sourced from the World Cancer Research Fund's (WCRF) NOURISHING Framework. Ten key informant interviews were conducted with former project members to refine and validate the lessons learned. Lessons learned were organized into a final list of critical success factors and themed into overarching categories. Outcomes: The majority of the food environment and healthy eating policy outcomes from CLASP occurred in workplace settings (n=133) and municipalities (n=111), and the least in schools/child care settings (n=16). The most frequent NOURISHING policy lever was “Offer healthy food and set standards in public institutions and other specific settings” primarily through policies to ban the sale of energy drinks (n=83) and implementing nutrition standards (n=58). Ten critical success factors were identified and described within three categories: people (n=3); tools (n=3); and approaches and ways of working (n=4). What was learned: A key takeaway from this work was a combination of cross-sectoral partnerships, tools and evidence, and collaborative ways of working were crucial to advance food environment and healthy eating policy change in municipalities, schools and child care settings, and workplaces. By utilizing the international WCRF NOURISHING Framework, it is intended that the lessons learned from this policy work in a Canadian context can inform local-level cancer prevention policy efforts around the world.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".