Exploring the Perspectives of Hemodialysis Nurses in Supporting Patient Coping and Resilience: An Interpretive Description Study
Bibliographic record
Abstract
Chronic kidney disease (CKD) is on the rise, as is the prevalence of people with end-stage CKD reliant on hemodialysis (HD) treatment. This is concerning as the burden of disease of CKD, especially in conjunction with HD, is substantial. From a literature review, there is a knowledge gap in HD nurses’ perspectives in supporting patient coping and resilience. HD nurses spend the most clinical time with HD patients, in an ideal position to support patient coping and resilience. The initial research question was: “What are HD nurses’ perspectives on supporting patient coping and resilience when caring for CKD patients receiving chronic HD treatment?” The qualitive methodology of Interpretive Description (ID) was used. Recruitment occurred at a provincial level within Alberta Kidney Care. Semi-structured interviews were conducted with HD nurses (Registered Nurses and Licensed Practical Nurses, n = 12) working in HD with >2 years of HD experience. In tandem with ID, Braun and Clarke (2004)’s method of descriptive thematic analysis was used in data analysis and code generation. During data collection, the research question evolved to: “What negotiations in care do HD nurses experience in striving to support patient coping and resilience for CKD patients on chronic HD treatment?” HD nurses experience four types: nursing perspectives <-> patient perspectives; medical care <-> psychological care; professional boundaries <-> therapeutic relationship; and organizational considerations <-> patient-centered care. Overall, moral distress was prevalent among HD nurses’ negotiations in care. Tailored initiatives to alleviate these specific negotiations may help to assuage this moral distress.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".