The Space in Between: Healthy Public Policy Development in Canada’s Provincial North
Bibliographic record
Abstract
Healthy public policy is a cornerstone of public health practice, and a key strategy for creating enabling environments for health. A recent priority has been to better account for and integrate context into healthy public policy-making, including in the design, execution and evaluation of population health interventions. Healthy Public Policy may be an important tool for promoting health in the smaller cities and rural regions that comprise Canada’s Provincial North. Populations living in these regions share common health challenges and bear a disproportionate burden of poorer health outcomes compared to their urban counterparts. The objective of this research was to explore healthy public policy development for smaller cities and rural regions. An empirical focus on retail food environments in Canada’s Provincial North captured the contextual scale needed for this research aim. Through a community-university partnership in Northern British Columbia, I asked: (1) How is context accounted for and integrated in population health intervention planning? (2) What features of local context are most important to measure to build an evidence-base for action? I employed an interpretivist methodology and a mixed-methods approach, adopting a exploratory sequential design. Over the course of two years embedded in the field, I engaged in participant observation, and conducted workshops and key informant interviews to design a locally-relevant retail food environment assessment plan, then conducted environmental observation of stores to quantify features. Finally, I completed in-depth interviews with retail store operators to further examine qualitatively their experiences of contextual factors. I found that institutions, settings and communities are organized according to the regional connectedness of smaller cities and adjacent rural regions, which has implications for healthy public policy-making in Canada’s Provincial North. Regional connections engender contextual features that are important to measure and understand, but capacity challenges in public health as well as retail practice limit data collection and evidence use at this scale. Existing strategies such as partnerships and community-engagement planning utilized by public health practitioners should be preserved. Additionally, new partnerships with retailers are needed to identify contextual priorities for future action.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".