Exploring person-centered integrated kidney care: Insights from a Canadian study
Notice bibliographique
Résumé
Introduction: One in ten Canadians face kidney disease. Both patients and providers have been calling for strategies to address the needs of individuals with early-stage chronic kidney disease. This population faces a high burden of multimorbidity (occurrence of two or more chronic conditions) and requires coordinated care across multiple providers and healthcare settings. However, there is a risk of fragmented care when coordinating across primary and nephrology care sectors, which may lead to poor access to and integration of services for patients. Person-centered integrated care (PC-IC) is a recognized approach for enhancing the management of chronic kidney disease and improving health outcomes. Nevertheless, there is limited evidence available to guide the delivery of PC-IC for this specific population. Audience: Our aim is to provide insight into the perspectives of patients with early-stage chronic kidney disease and multimorbodity, along with their caregivers and healthcare providers. Team: We have assembled a strong team of researchers, clinicians, and patient partners. Our team brings complementary skills for conducting the proposed research, including expertise in quantitative, qualitative, and mixed methods, patient-orientated research, primary care and nephrology research in remote, rural, and urban settings, integrated care, and health economics and service delivery. Two of our patient partners, Ms. Verdin and Ms. Russon, actively participate in the planning and execution of this study. They are actively involved in research team meetings and play a crucial role in promoting the dissemination of our research findings. Methods: We conducted a cross-sectional survey study using the Rainbow Model of Integrated Care Measurement Tools (RMIC-MTs). Our recruitment efforts targeted patients, caregivers, and healthcare providers through various channels, including networks, social media, and direct referrals from healthcare professionals within Health Services and Primary Care Networks in Alberta. We conducted descriptive analyses to detect variances tied to respondent roles and background characteristics. Additionally, the integrated case assessments were examined and compared to those of an international collaborative network of dialysis clinics in 23 different countries. Results: During the conference, we will present the preliminary findings regarding the perceptions of integrated renal care among patients and healthcare providers in Alberta. We will also provide a comparative analysis with an international renal care network. Furthermore, we will unveil and discuss variations in integrated care perspectives among subgroups, considering the roles and background characteristics of the participants. Discussion: This research enhances our understanding of the challenges and opportunities, both at the national and international levels, associated with delivering person-centered integrated renal care. The insights gained from this study will serve as a foundational element for a patient-oriented research initiative aimed at collaboratively devising an innovative approach to PC-IC. In the subsequent phase of our work, we will identify and prioritize barriers and facilitators that impact PC-IC in Alberta, Canada. This will be accomplished through qualitative interviews and the application of a modified Nominal Group Technique.
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 | 0,021 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,011 |
| Études des sciences et des technologies | 0,032 | 0,007 |
| Communication savante | 0,009 | 0,004 |
| Science ouverte | 0,004 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».