Patient, Caregiver, and Provider Perspectives on Improving Information Delivery in Hemodialysis: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients with kidney failure are exposed to a surfeit of new information about their disease and treatment, often resulting in ineffective communication between patients and providers. Improving the amount, timing, and individualization of information received has been identified as a priority in in-center hemodialysis care. OBJECTIVE: To describe and explicate patient, caregiver, and health care provider perspectives regarding challenges and solutions to information transfer in clinical hemodialysis care. DESIGN: In this multicenter qualitative study, we gathered perspectives of patients, their caregivers, and health care providers conducted through focus groups and interviews. SETTING: Five Canadian hemodialysis centers: Calgary, Edmonton, Winnipeg, Ottawa, and Halifax. PARTICIPANTS: English-speaking adults receiving in-center hemodialysis for longer than 6 months, their caregivers, and hemodialysis health care providers. METHODS: Between May 24, 2017, and August 16, 2018, data collected through focus groups and interviews with hemodialysis patients and their caregivers subsequently informed semi-structured interviews with health care providers. For this secondary analysis, data were analyzed through an inductive thematic analysis using grounded theory, to examine the data more deeply for overarching themes. RESULTS: Among 82 patients/caregivers and 31 healthcare providers, 6 main themes emerged. Themes identified from patients/caregivers were (1) overwhelmed at initiation of hemodialysis care, (2) need for peer support, and (3) improving comprehension of hemodialysis processes. Themes identified from providers were (1) time constraints with patients, (2) relevance of information provided, and (3) technological innovations to improve patient engagement. LIMITATIONS: Findings were limited to Canadian context, English speakers, and individuals receiving hemodialysis in urban centers. CONCLUSIONS: Participants identified challenges and potential solutions to improve the amount, timing, and individualization of information provided regarding in-center hemodialysis care, which included peer support, technological innovations, and improved knowledge translation activities. Findings may inform the development of interventions and strategies aimed at improving information delivery to facilitate patient-centered hemodialysis care.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".