Design and Development of a Digital Counseling Program for Chronic Kidney Disease
Bibliographic record
Abstract
Background: Self-management has shown to improve the quality of life in patients with chronic kidney disease (CKD). Readily accessible self-management tools are essential in promoting adherence to self-care behaviors. In recognizing that digital health facilitates efficient access to self-management programs, we developed a digital counseling program, ODYSSEE Kidney Health, to promote self-care behaviors while supporting health-related quality of life. Objective: To present the design and development of ODYSSEE Kidney Health for digital counseling for patients with CKD. Design: The study involved an iterative design process based on user-centered design principles to develop the digital counseling program, ODYSSEE Kidney Health. Setting: A sample of 10 to 15 participants were purposively sampled from nephrology clinics at the University Health Network, Toronto, Canada. Methods: Participants underwent 2 phases in the development process. In each phase, participants were presented with a component of the program, asked to perform goal-oriented tasks, and participate in the "think-aloud" process. Semi-structured interviews followed the first phase to identify feedback about the overall program. Thematic analysis of the interviews identified themes from the usability testing. Descriptive statistics were used to summarize patient demographic data. Results: We enrolled 11 participants (n = 7 males, n = 4 females, ages 30-82). The main themes generated anchored on (1) impact on nephrology care, (2) technical features, and (3) CKD content. Overall, participants reported positive satisfaction toward the navigation, layout, and content of the program. They cited the value of the program in their daily CKD care. Limitations: Study limitations included using a single center to recruit participants, most of the participants having prior technology use, and using one module as a representative of the entire digital platform. Conclusion: The acceptability of a digital counseling program for patients with CKD relies on taking the patients' perspective using a user-centered design process. It is vital in ensuring adoption and adherence to self-management interventions aimed at sustaining behavioral change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".