Advancing Health Equity Through Primary Care: Protocol for the Spread, Scale, and Multimethod Developmental Evaluation of the Deep End Canada Network (Preprint)
Notice bibliographique
Résumé
BACKGROUND The social determinants of health are “the conditions in which people are born, grow, live, work and age,” such as housing, employment, and race. Canadian primary health care organizations are increasingly looking for ways to systematically and routinely collect demographic and social needs data from patients to increase appropriate and responsive care by attending to the social determinants of health. The SPARK (screening for poverty and related determinants to improve knowledge of and links to local resources) tool is a standardized tool for use in primary health care settings that was developed, pilot-tested, refined, and validated in primary health care clinics across Canada. General Practitioners at the Deep End is a network of primary health care general practitioners working in the 100 most socioeconomically deprived areas in Scotland with the goal of connecting practitioners and advocating for better training, policies, and resources. Primary Health Care at the Deep End Canada or Soins primaires en milieux défavorisés Canada (Deep End Canada) was launched in June 2024 to offer guidance and resources to the growing number of primary health care teams interested in collecting and using demographic and social needs data. OBJECTIVE Our primary objective is to spread the sustainable implementation of the SPARK tool to systematically and routinely collect and use demographic and social needs data to respond to the social determinants of health in 20 to 25 primary health care organizations across Canada through the creation of Deep End Canada. METHODS Deep End Canada is a network of primary health care teams, including health professionals, researchers, and patient partners, working in areas with high rates of poverty. This project will use a multimethod developmental evaluation approach guided by the reach, effectiveness, adoption, implementation, and maintenance framework. Web-based surveys will capture the reach and adoption of demographic and social needs data collection, and network activities will be evaluated using qualitative analysis of focus groups, interviews, and meetings. RESULTS The study was funded in February 2022. Recruitment to the network commenced in June 2024 and included 10 organizations as of submission of the manuscript. Web-based survey data collection commenced in September 2024. Implementation of the network will be assessed from June 2024 to December 2025, with expected findings available in June 2026 and published in the fall of 2026. CONCLUSIONS Efforts to systematically and routinely collect and use demographic and social needs data and address structural issues in Canadian primary health care have grown significantly in the last 10 years. This increased commitment involves using data to develop evidence-based interventions that can reduce health inequities and address local community needs at the individual, organizational, and policy levels. This study will demonstrate how the Deep End Canada network contributes to achieving those aims by fostering collaboration among practitioners to promote health equity and tackle the structural and social determinants of health. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/75732
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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 | 0,203 | 0,167 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,006 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,008 | 0,015 |
| Études des sciences et des technologies | 0,013 | 0,006 |
| Communication savante | 0,011 | 0,006 |
| Science ouverte | 0,009 | 0,012 |
| Intégrité de la recherche | 0,005 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,111 | 0,017 |
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 ».