Exploring person-centered integrated kidney care: Insights from a Canadian study
Bibliographic record
Abstract
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.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".