Preferences for a self-management e-health tool for patients with chronic kidney disease: results of a patient-oriented consensus workshop
Bibliographic record
Abstract
BACKGROUND: Electronic health (e-health) tools may support patients' self-management of chronic kidney disease. We aimed to identify preferences of patients with chronic kidney disease, caregivers and health care providers regarding content and features for an e-health tool to support chronic kidney disease self-management. METHODS: A patient-oriented research approach was taken, with 6 patient partners (5 patients and 1 caregiver) involved in study design, data collection and review of results. Patients, caregivers and clinicians from across Canada participated in a 1-day consensus workshop in June 2018. Using personas (fictional characters) and a cumulative voting technique, they identified preferences for content for 8 predetermined topics (understanding chronic kidney disease, diet, finances, medication, symptoms, travel, mental and physical health, work/school) and features for an e-health tool. RESULTS: There were 24 participants, including 11 patients and 6 caregivers, from across Canada. The following content suggestions were ranked the highest: basic information about kidneys, chronic kidney disease and disease progression; reliable information on diet requirements for chronic kidney disease and comorbidities, renal-friendly foods; affordability of medication, equipment, food, financial resources and planning; common medications, adverse effects, indications, cost and coverage; symptom types and management; travel limitations, insurance, access to health care, travel checklists; screening and supports to address mental health, cultural sensitivity, adjusting to new normal; and support to help integrate at work/school, restrictions. Preferred features included visuals, the ability to enter and track health information and interact with health care providers, "on-the-go" access, links to resources and access to personal health information. INTERPRETATION: A consensus workshop developed around personas was successful for identifying detailed subject matter for 8 predetermined topic areas, as well as preferred features to consider in the codevelopment of a chronic kidney disease self-management e-health tool. The use of personas could be applied to other applications in patient-oriented research exploring patient preferences and needs in order to improve care and relevant outcomes.
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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 | 0.042 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".