042 Decision support interventions for dialysis choice: a structured process of shared decision making for a patient with chronic kidney disease. A case study
Bibliographic record
Abstract
Introduction Patients with chronic kidney disease (CKD) face the difficult decision about choosing type of dialysis. The purpose of this case study was to explore the use of a decision support intervention provided by a nurse recently trained in decision coaching. Methods A case study was conducted guided by the Ottawa Decision Support Framework. The eligible participant had to be experiencing decisional conflict about choosing dialysis modality. The nurse trained in decision coaching assessed the participant’s decisional needs, provided decision coaching using the Ottawa Personal Decision Guide, and screened for decisional conflict using a 4-item SURE Test. Descriptive analysis was used to measure change in decisional needs and quality of decision coaching using the Decision Support Analysis Tool (DSAT). Results An 81 yr. with stage 5 CKD showed uncertainty regarding his dialysis choices (home haemodialysis, centre haemodialysis, peritoneal dialysis). He previously received standardized education and discussions at medical appointments. After decision coaching (51 minutes), he scored 4 on 4 for the SURE Test. He reported feeling supported by his family and no pressure to choose an option. The patient was satisfied with decision coaching. The nurse scored 8 on 10 for the DSAT (lost points regarding the initial identification of uncertainty and intervening about potential harms). Discussion Decision coaching by an experienced CKD nurse, newly trained in decision coaching helped resolve decisional needs and the patient reported a positive experience. The nurse should enhance her skills in clearly initiating the decision coaching and confidence intervening regarding potential harms of treatments. This intervention fit within the Australian CKD healthcare context. Conclusion A structured approach can be used to assist CKD patients making dialysis choices based on what matters most to them. Decision coaching with a trained CKD nurse should be evaluated as part of the CKD model of 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".