“We’re not There yet”: Exploring Contextual Factors Shaping Canadian Dialysis Nurses’ Engagement in Kidney Supportive Care
Bibliographic record
Abstract
Abstract: Treatment for kidney failure, such as dialysis, can result in a tremendously high physical and psychosocial symptom burden on patients and their families. Kidney supportive care (KSC), including advance care planning, involves early identification and treatment of symptoms that improve the quality of life for people receiving dialysis. The delay or lack of engagement in KSC by dialysis nurses until the end of life may result in people dying without receiving optimal palliative care services. Purpose and Questions: Our overarching purpose is to develop a theory about the process of engagement in KSC by dialysis nurses, and this paper is about a sub-question: What are the personal, professional, organizational, and environmental factors that shape nurses’ attitudes/beliefs toward and knowledge of supportive care in dialysis? Methods: We followed Charmaz’s constructivist grounded theory method. Through initial purposeful and subsequent theoretical sampling, 23 nurses with work experience in outpatient hemodialysis, home hemodialysis, and peritoneal dialysis settings from across Canada were recruited to participate in two interviews, each using the Zoom© teleconferencing platform. Concurrent data collection and analysis were undertaken. Results: Findings at the focused coding stage comprise contextual factors impacting such engagement. The core category of Fragmenting Care is explained by four categories of contextual factors and their related concepts and sub-concepts: (1) structural (lack of dedicated time, language barrier, knowledge gap); (2) inter-relational (patient-related factors; nurse-related factors [discomfort with having the conversation, lack of self-confidence, multi-dimensional tensions—them versus us]); (3) cultural-dialysis (biomedical focus, ambiguous responsibility, inopportune conversations); and (4) systemic (lack of conceptual clarity). Implications: These collective factors have not been illuminated previously, and while challenging, they help to better understand and therefore address engagement in KSC by dialysis nurses. Conclusion: Effecting change to normalize KSC is a priority requiring solutions compatible with complex systems.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".