Perspectives of Clinicians on Shared Decision Making in Pediatric CKD: A Qualitative Study
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Clinical decision-making priorities may differ among children, their parents, and their clinicians. This study describes clinicians' perspectives on shared decision making in pediatric chronic kidney disease (CKD) and identifies opportunities to improve shared decision making and care for children with CKD and their families. STUDY DESIGN: Semistructured interviews. SETTING & PARTICIPANTS: Fifty clinicians participated, including pediatric nephrologists, nurses, social workers, surgeons, dietitians, and psychologists involved in providing care to children with CKD. They worked at 18 hospitals and 4 university research departments across 11 countries (United States of America, Canada, Australia, People's Republic of China, United Kingdom, Germany, France, Italy, Lithuania, New Zealand, and Singapore). ANALYTICAL APPROACH: Interview transcripts were analyzed thematically. RESULTS: We identified 4 themes: (1) striving to blend priorities (minimizing treatment burden, emphasizing clinical long-term risks, achieving common goals), (2) focusing on medical responsibilities (carrying decisional burden and pressure of expectations, working within system constraints, ensuring safety is foremost concern), (3) collaborating to achieve better long-term outcomes (individualizing care, creating partnerships, encouraging ownership and participation in shared decision making, sensitive to parental distress), and (4) forming cumulative knowledge (balancing reassurance and realistic expectations, building understanding around treatment, harnessing motivation for long-term goals). LIMITATIONS: Most clinicians were from high-income countries, so the transferability of the findings to other settings is uncertain. CONCLUSIONS: Clinicians reported striving to minimize treatment burden and working with children and their families to manage their expectations and support their decision making. However, they are challenged with system constraints and sometimes felt the pressure of being responsible for the child's long-term outcomes. Further studies are needed to test whether support for shared decision making would promote strategies to establish and improve the quality of care for children with CKD.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".