Measuring person-centered integrated care for people living with mild to moderate chronic kidney disease and multimorbidity: a cross-sectional survey
Bibliographic record
Abstract
Introduction Person-centered integrated care (PC-IC) has been shown to improve health outcomes for individuals with chronic conditions. However, there is limited evidence measuring PC-IC delivery to people with mild to moderate chronic kidney disease and co-morbidities. We aimed to assess PC-IC delivery for this population in Alberta, Canada. Methods We conducted a survey (May-December 2023) using the Rainbow Model of Integrated Care Measurement Tool via weblink or telephone to quantify PC-IC using a 5-point Likert agreement scale. Patients with chronic kidney disease (non-dialysis, non-transplant) and co-morbidities, caregivers, and health care providers in Alberta were invited to participate. Participants were recruited through various methods, including in-clinic posters and web-based posts. We assessed responses using descriptive and non-parametric analyses (e.g., Mann–Whitney U -test). Results Ninety-seven eligible individuals completed the survey; 24 patients, 12 caregivers, and 61 health care providers. Caregivers rated PC-IC significantly lower than patients (overall score: 3.36/5 and 3.91/5, respectively, p < 0.05) and health care providers rated PC-IC moderately (3.56/5). The lowest scored domain was care coordination amongst patients and caregivers (3.43/5 and 3/5, respectively, p < 0.05) and regional health care laws/regulations amongst health care providers (2.94/5). Conclusion Survey respondents recognized that the overall delivery of PC-IC is not optimal and identified key areas to address including improving care coordination (e.g., communication between providers) and tackling regional health care laws/regulations (e.g., funding models). Our study highlights the need for further exploration regarding why PC-IC is perceived as suboptimal, particularly among subgroups, and how it can be improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".