Priorities for peer support delivery among adults living with chronic kidney disease: a patient-oriented consensus workshop
Bibliographic record
Abstract
BACKGROUND: Peer support can address the informational and emotional needs of people living with chronic kidney disease (CKD) and enable self-management. We aimed to identify preferences and priorities for content, format and processes of peer support delivery for patients with non-dialysis CKD and their loved ones. METHODS: Using a patient-oriented research approach, we conducted a half-day, virtual consensus workshop with stakeholder participants from across Canada, including patients, caregivers, peer mentors and clinicians. Using personas (fictional characters), participants discussed and voted on preferences for delivery of peer support across format, content and process categories. We analyzed transcripts from small- and large-group discussions inductively using content analysis. RESULTS: Twenty-one stakeholders, including 9 patients and 4 caregivers, participated in the workshop. In the voting exercise on format, participants prioritized peer mentor matching, programming for both patients and caregivers, and flexible scheduling. For content, participants prioritized informational and emotional support focus, and for process, they prioritized leveraging kidney care programs and alternative sources (e.g., social media) for promotion and referral. Analysis of workshop transcripts complemented prioritization results and emphasized tailoring of peer support delivery to accommodate the diversity of people living with CKD and their support needs. This concept was elaborated in 3 themes, namely alignment of program features with needs, inclusive peer support options and multiple access points. INTERPRETATION: We identified preferences for peer support delivery for people living with CKD and underscore the importance of tailored, flexible programming in this context. Findings could be used to develop, adapt or study CKD-focused peer support interventions.
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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 | 0.073 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| 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".