The imperative of working with municipalities to create an integrated health and wellness system.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,018 | 0,009 |
| Communication savante | 0,020 | 0,009 |
| Science ouverte | 0,003 | 0,022 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».