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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".