Enhancing Self-management Support for Adults with Chronic Kidney Disease: A Person-centered, Theory-informed Approach
Bibliographic record
Abstract
Strategies to support patients to self-manage their chronic kidney disease (CKD) have been identified as one of the top 10 kidney research priorities internationally. Recognizing that this research priority is key to delivering person-centered care, this thesis examines enhancing adult CKD self-management support using patient-centered and theoretical approaches. We conducted three sequential studies: a scoping review to identify and describe self-management interventions for adult patients with CKD; a descriptive qualitative study to identify the needs of adults with CKD and their caregivers based on their experiences with managing CKD; and a one-day consensus workshop using personas to determine the preferences for content and features for a CKD patient self-management electronic health (eHealth) tool. We found a lack of patient engagement and application of behaviour change theories in the development of CKD self-management interventions. In addition, we identified the needs of patients and their caregivers regarding areas of knowledge, information sharing, and relevant supports for self-management in early stages of CKD. Patients, caregivers, health care professionals, and policy makers provided detailed subject matter for CKD topic areas, as well as preferred features to consider for a novel approach to supporting CKD self-management. This thesis work is pragmatic, as well as innovative in nature. To our knowledge, this is the first multi-phase study to meaningfully engage patients with CKD and caregivers as patient partners. Their involvement went beyond the role of consultation, where they actively participated in informing all phases of the research. To enhance CKD self-management support, our work offers evidence to inform the co-development of a tailored self-management support intervention for adults with CKD and their informal caregivers in Canada.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".