Exploring Haemodialysis Nurses' Perceptions on Kidney Replacement Therapy Modality Education: A Framework Analysis
Bibliographic record
Abstract
BACKGROUND: Many people with kidney failure start and remain on in-centre haemodialysis treatment despite evidence of improved outcomes with home dialysis. To make an informed modality decision patients must receive frequent, high-quality modality education. This education is inconsistent in the in-centre haemodialysis setting, where patients spend the most time with nurses while receiving haemodialysis treatments. OBJECTIVES: The aim of this study was to examine in-centre haemodialysis nurses' perceptions around modality education for patients receiving in-centre haemodialysis using the COM-B model of behaviour change. DESIGN: We used framework analysis as a research method, applying the COM-B model as a theoretical framework to understand nurses' perceptions of modality education. PARTICIPANTS: We interviewed 13 in-centre haemodialysis nurses in a single province in Canada. APPROACH: We completed semi-structured interviews via Zoom, which ranged from 30 to 60 min. FINDINGS: Participants reported knowledge deficits, lack of experience or exposure to other dialysis modalities, and lack of resources to support modality education practices. In-centre haemodialysis nurses reported some factors that enhanced modality education, including strong nurse-patient therapeutic relationships and previous experience in other dialysis modalities. CONCLUSIONS: Nurses could have a role in modality education but had different views on what this role should be. Nurses faced barriers in modality education such as knowledge deficits, a lack of experience with home modalities, and limited patient teaching resources. Factors that favoured modality education were strong nurse-patient relationships and previous experience with other modalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".