Attitudes and perceptions of nephrology nurses towards dialysis modality selection: a survey study
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information about the views of dialysis nurses towards dialysis modality selection, yet nurses often have the most direct contact time with patients. We conducted a survey to better understand nurses' attitudes and perceptions, and hypothesized that nurses with different areas of expertise would have differences in opinions. METHODS: We administered an electronic survey to all dialysis/predialysis nurses (n = 129) at a large, tertiary care center. The survey included questions about preferred therapy - in-center hemodialysis (CHD), versus home dialysis (home hemodialysis and peritoneal dialysis) and ideal modality mix. Responses were compared between nurses with home dialysis and CHD experience. RESULTS: The survey response rate was 69%. Both nursing groups ranked patient caregivers and dialysis nurses as having the least impact on patient modality selection. For most patient characteristics (including age > 70 years and presence of multiple chronic illnesses), CHD nurses felt that CHD was somewhat or strongly preferred, while home dialysis nurses preferred a home modality (p < 0.001 for all characteristics studied). Similar differences in responses were noted for patient/system factors such as patient survival, cost to patients and nursing job security. Compared to CHD nurses, a higher proportion of home dialysis nurses felt that CHD was over-utilized (85% versus 58%, p = 0.024). CONCLUSION: Dialysis nurses have prevailing views about modality selection that are strongly determined by their area of experience and expertise.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".