Decision coaching for people with kidney failure: A case study
Bibliographic record
Abstract
BACKGROUND: Little is known about the usefulness of decision coaching for people with kidney failure facing decisions about end-of-life care. OBJECTIVES: To investigate experiences of people with kidney failure who received decision coaching for end-of-life care decisions. DESIGN: We conducted a prospective case study bound by time (September to December 2021), location (one nephrology department), and guided by the Ottawa Decision Support Framework. PARTICIPANTS: Adults with kidney failure facing end-of-life care decisions. MEASUREMENTS: A nurse trained in decision coaching screened for unmet decisional needs with the SURE test and provided decision coaching using the Ottawa Personal Decision Guide. Postcoaching, the participants were rescreened using the SURE test and interviewed to explore their experience with decision coaching. Change in SURE test findings was analysed descriptively and systematic text condensation was used for the analysis of interviews. Recorded decision coaching sessions underwent content analysis using the Decision Support Analysis Tool. RESULTS: Decision coaching was provided to four adults with kidney failure. Median pre-SURE test score was 2.5 (range 2-4) and posttest score was 3 (range 3-4), indicating a decrease in decisional needs. Participants described that decision coaching provided an overview of features of options to consider, identified remaining decisional needs for further discussion with relatives and health professionals and clarified next steps. Median Decision Support Analysis Tool score was 9 (range 8-9). CONCLUSIONS: After decision coaching, results suggest that the participants experienced fewer decisional needs and seemed clearer about the next steps in the decision making process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".