Multilevel estimation of the relative impacts of social determinants on income-related health inequalities in urban Canada: Protocol for the Canadian Social Determinants Urban Laboratory
Notice bibliographique
Résumé
Abstract Building on Canadian data at the provincial, regional, community, and personal levels, the Canadian Social Determinants Urban Laboratory (CSDUL) will enable multilevel and longitudinal investigation of how social determinants of health (SDOH) impact population health (both mental and physical) and health inequities in Canada. Utilizing administrative data linkage, CSDUL will be developed by combining social, economic, and political mechanisms at multiple levels, from national to individual, following the World Health Organization (WHO) SDOH framework. Organized using a hub-and-node model, CSDUL will be created by validating unit and area-level indicators and merging survey and administrative data to provide a comprehensive understanding of SDOH at micro, meso, and macro levels. The project will replicate WHO/Europe’s decomposition analysis of income-related inequalities in self-reported health, assessing the relative impact of social determinants on health outcomes. Extended Abstract Introduction Two decades of research have highlighted persistent income-related health inequities in Canada at municipal, provincial, and national levels. This project aims to examine how social, economic, and political factors create conditions that shape health inequalities, and investigate how structural and intermediate determinants explain health disparities across national, provincial, city, neighbourhood, and individual levels. Methods We will create the Canadian Social Determinants Urban Laboratory (CSDUL), a multilevel, longitudinal virtual environment combining multiple surveys and administrative databases, guided by the WHO Social Determinants of Health framework. Initially covering 2011-2015, CSDUL will expand as more data becomes available. Organized in a hub-and-node model, it will include a central hub and five project nodes. We will develop and validate area-based indicators, merged with data to provide a comprehensive understanding of social determinants of health at micro, meso, and macro levels 1 . Results The primary research deliverables of this project will be to critically analyze the strengths and limitations of survey and administrative databases for health research and develop methods for deriving variables from them. After developing CSDUL, we will replicate WHO/Europe’s income-related health inequality analysis for urban Canada and report on the impact of social determinants on health outcomes. Discussion A key strength of the proposed virtual data laboratory is its ability to examine how various determinants affect health at different levels and explore their impact on identifiable groups (e.g., by gender). It highlights the multifactorial nature of health and identifies the factors most likely to drive health outcomes, such as what makes Canadians healthy or sick. Conclusion Multisectoral interventions are most effective when they are customized to meet the unique needs of specific sub-populations, using robust and multilevel data sources like CSDUL. Key Messages Building on Canadian data, the Canadian Social Determinants Urban Laboratory (CSDUL) is the first initiative of its kind to provide a comprehensive understanding of how social determinants impact health outcomes in Canadian cities. CSDUL will be a multilevel, longitudinal data Laboratory, organized using a hub-and-node model, operationalizing the WHO Social Determinants of Health framework After developing CSDUL, we will replicate WHO/Europe’s decomposition analysis of income-related inequalities in self-reported health for urban Canada
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,029 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,011 | 0,002 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,006 |
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 ».