The imperative of working with municipalities to create an integrated health and wellness system.
Bibliographic record
Abstract
Introduction: It is estimated that 80% of modifiable health relates to social determinants and the built environment. It is also estimated that in Canada, 90% of municipal spending directly or indirectly impacts such determinants (Association of Municipalities of Ontario, 2022). Accordingly, it could be said that municipalities determine 70% of our health. In system-level integrated care, we typically have interactions with a variety of actors, but municipal governments are often overlooked. In this initiative, we describe how we purposefully engaged with municipal governments to support health and wellness in their jurisdictions. Target audience: This topic is pertinent to those working at a systems level in healthcare, community development, government and the third sector. Who was engaged? In developing this approach, health system planners, academic experts, those involved in community development and citizens of communities in Alberta, Canada were engaged in developing a shared understanding and approach. What was done? With over 300 municipalities in of Alberta, it was not possible to meet with all. Hence, our Health System Integration portfolio targeted the larger municipalities, overarching municipal organizations and key opinion leaders. Our first action was to catalogue the categories and ways in which municipal policies and actions impact health (for example, transportation, housing, social connectedness). We then supported these categories with examples and with the scientific proof demonstrating benefit. Two shared documents were developed. The first document provided a listing of municipal activities and policies shown to be of benefit in facilitating health and wellness with supporting evidence. A second document was a “top 10” listing of the most impactful actions a municipality could take to improve the health and wellness of its citizens. In our meetings, an asset-based community development approach was highlighted, emphasizing the effectiveness and low-cost of such. Results: Without exception, our engagement was met with interest and enthusiasm. Frequently we were connected to other individuals who shared this mutual interest or who were involved in endeavors related to health and wellness. Often, these individuals were in a decision-making capacity and were able to directly enact policy changes. Currently, our perception is that there is rapidly spreading awareness. This is based on concrete actions we have observed as well as new positions hired within municipalities related to the creation of wellness or community development. Learnings: First, relationships were key. Identifying key opinion leaders, influencers and key organizations was crucial. Secondly, presenting benefits from the perspective of the municipality rather than the health system was of great importance. Third, working with the willing led to an organic, grassroots spread of support and understanding, in keeping with rhizome theory. In fact, one city councillor even used the term “this is spreading from town to town like the rhizomes in soil”. Next steps: Our goal is to continue supporting municipalities. We are also highlighting the role of municipalities to other actors involved in health to support large-scale integration. Finally, we are developing shared measures to highlight the cross-sector benefit of integrated care.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".