Exploring the roles of urban municipal governments in addressing population health inequities: prescriptions, capacities, and intentions
Bibliographic record
Abstract
The 1986 Ottawa Charter for Health Promotion inspired an outpouring of research on the social determinants of health (SDOH) and health inequities (HI), much of it implicating cities as important sites for intervention. Health promotion and the Healthy Cities movement constituted an initial wave of research activity, while population health and urban health dominated a second wave. Yet, this research has inadequately translated into policy action on HI in Canada, and the roles that urban municipalities can play in addressing HI are vague. Thus, the objectives of this mixed-methods research program were threefold: 1) to identify prescriptions made in the HI literatures for municipal government intervention; 2) to assess capacities for intervention based on perceptions held by influential municipal actors; and 3) to assess intentions of selected Metro Vancouver municipalities to address HI in their jurisdictions. Phase One reviewed article abstracts from the HI literatures for SDOH profiles and prescriptions for municipal intervention. Phase Two surveyed politicians and senior-level staff of Metro Vancouver municipalities regarding their views on the SDOH and roles of municipal governments in addressing HI. Phase Three examined Official Community Plans (OCPs) of five Metro Vancouver municipalities for intentions to engage in interventions prescribed in Phase One. Prescriptions articulated for municipal intervention included health-focused interventions, relationship building, and delivering on slated responsibilities. Capacities and intentions for intervention on HI were mixed. Actors identified numerous existing policies that could reduce HI, and governments articulated strong intentions to deliver on slated responsibilities and engage citizens. In contrast, indicators of low capacity arose from actors’ individualistic SDOH attitudes and perceptions that municipal governments bear little responsibility for HI, and from governments’ limited intention to build community partnerships, or engage in health-focused interventions. Common themes were incongruence between scholarly and policy discourse on HI and municipal interventions; individualistic and environmental perspectives towards the SDOH and HI; engaging in status quo interventions and forming relationships with other governments; and concerns about offloading of responsibilities from senior governments. More critical perspectives on challenges to knowledge translation are needed if municipalities are expected to address local HI.
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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 | 0.000 | 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".