Patient, Caregiver, and Provider Perspectives on Challenges and Solutions to Individualization of Care in Hemodialysis: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Clinical settings often make it challenging for patients with kidney failure to receive individualized hemodialysis (HD) care. Individualization refers to care that reflects an individual's specific circumstances, values, and preferences. OBJECTIVE: This study aimed to describe patient, caregiver, and health care professional perspectives regarding challenges and solutions to individualization of care in people receiving in-center HD. DESIGN: In this multicentre qualitative study, we conducted focus groups with individuals receiving in-center HD and their caregivers and semi-structured interviews with health care providers from May 2017 to August 2018. SETTING: Hemodialysis programs in 5 cities: Calgary, Edmonton, Winnipeg, Ottawa, and Halifax. PARTICIPANTS: Individuals receiving in-center HD for more than 6 months, aged 18 years or older, and able to communicate in English were eligible to participate, as well as their caregivers. Health care providers with HD experience were recruited using a purposive approach and snowball sampling. METHODS: Two sequential methods of qualitative data collection were undertaken: (1) focus groups and interviews with HD patients and caregivers, which informed (2) individual interviews with health care providers. A qualitative descriptive methodology guided focus groups and interviews. Data from all focus groups and interviews were analyzed using conventional content analysis. RESULTS: Among 82 patients/caregivers and 31 health care providers, we identified 4 main themes: session set-up, transportation and parking, socioeconomic and emotional well-being, and HD treatment location and scheduling. Particular challenges faced were as follows: (1) session set-up: lack of preferred supplies, machine and HD access set-up, call buttons, bed/chair discomfort, needling options, privacy in the unit, and self-care; (2) transportation and parking: lack of reliable/punctual service, and high costs; (3) socioeconomic and emotional well-being: employment aid, finances, nutrition, lack of support programs, and individualization of treatment goals; and (4) HD treatment location and scheduling: patient displacement from their usual spot, short notice of changes to dialysis time and location, lack of flexibility, and shortages of HD spots. LIMITATIONS: Uncertain applicability to non-English speaking individuals, those receiving HD outside large urban centers, and those residing outside of Canada. CONCLUSIONS: Participants identified challenges to individualization of in-center HD care, primarily regarding patient comfort and safety during HD sessions, affordable and reliable transportation to and from HD sessions, increased financial burden as a result of changes in functional and employment status with HD, individualization of treatment goals, and flexibility in treatment schedule and self-care. These findings will inform future studies aimed at improving patient-centered HD 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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".