A mixed-methods protocol to explore psychological distress and psychosocial needs along the continuum of CKD care— a single healthcare network
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) affects 8-16% of adults worldwide, including nearly 4 million Canadians. As the disease progresses, patients often experience a significant decline in quality of life (QoL), driven by disease-related symptoms and the burden of renal replacement therapies (RRT) such as dialysis and transplantation. In addition to physical symptoms, up to 55% of patients report psychological distress, including depression and anxiety. Despite this, mental health needs are frequently under-identified and insufficiently addressed in nephrology care. There is limited understanding of how patients experience and cope with this distress, how staff perceive their role in responding to it, and how healthcare environments support or hinder psychosocial care. This study seeks to address these gaps within a single integrated nephrology care network. METHODS: This explanatory sequential mixed-methods study will be conducted in two phases. In the first phase, quantitative data will be collected from 200 patients with CKD and 40-50 nephrology staff members using validated questionnaires assessing psychological distress, anxiety, depression, and QoL. Group comparisons will assess differences across RRT modalities, and association analyses will explore relationships between psychological outcomes and demographic or clinical factors. In the second phase, semi-structured interviews and focus groups with patients and staff will explore experiences of psychological distress and care delivery in greater depth. Thematic analysis will be used for qualitative data. Integration of findings through triangulation will provide a comprehensive understanding of psychosocial needs and inform intervention development. DISCUSSION: This study uses a mixed-methods design to investigate psychosocial needs in CKD care from both patient and provider perspectives. It aims to identify modifiable clinical and organizational factors-such as staff preparedness, referral practices, and systemic barriers-that shape the delivery of mental health support. Findings will inform the development of person-centered interventions and may guide broader models for integrating psychological care into chronic disease management. STATUS OF TRIAL: The study began in June 2024. Participant recruitment and data collection are ongoing and will continue until June 2026. CLINICAL TRIAL NUMBER: Not applicable.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".