Educational Support Around Dialysis Modality Decision Making in Patients With Chronic Kidney Disease: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease (CKD) are asked to choose a renal replacement therapy or conservative management. Education and knowledge transfer play key roles in this decision-making process, yet they remain a partially met need. OBJECTIVE: We sought to understand the dialysis modality decision-making process through exploration of the predialysis patient experience to better inform the educational process. DESIGN: Qualitative descriptive study. SETTING: Kidney Care Centre of London Health Sciences Centre in London, Ontario, Canada. PATIENTS: Twelve patients with CKD, with 4 patients on in-center hemodialysis, home hemodialysis, and peritoneal dialysis, respectively. MEASUREMENTS: Not applicable. METHODS: We conducted semistructured interviews with each participant, along with any family members who were present. Interviews were transcribed verbatim. Conventional content analysis was used to analyze the transcripts for common themes. Representative quotes were decided via team consensus. A patient collaborator was part of the research team. RESULTS: Three themes influenced dialysis modality decision making: (i) Patient Factors: individualization, autonomy, and emotions; (ii) Educational Factors: tailored education, time and preparation, and available resources; and (iii) Support Systems: partnership with health care team, and family and friends. LIMITATIONS: Sample not representative of wider CKD population. Limited number of eligible patients. Poor recall may affect findings. CONCLUSIONS: Modality decision making is a complex process, influenced by the patient's health literacy, willingness to accept information, predialysis lifestyle, support systems, and values. Patient education requires the flexibility to individualize the delivery of a standardized CKD curriculum in partnership with a patient-health care team, to fulfill the goal of informed, shared decision making.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".