Improving serious illness communication: a qualitative study of clinical culture
Bibliographic record
Abstract
Abstract Objective Communication about patients’ values, goals, and prognosis in serious illness (serious illness communication) is a cornerstone of person-centered care yet difficult to implement in practice. As part of Serious Illness Care Program implementation in five health systems, we studied the clinical culture-related factors that supported or impeded improvement in serious illness conversations. Methods Qualitative analysis of semi-structured interviews of clinical leaders, implementation teams, and frontline champions. Results We completed 30 interviews across palliative care, oncology, primary care, and hospital medicine. Participants identified four culture-related domains that influenced serious illness communication improvement: (1) clinical paradigms; (2) interprofessional empowerment; (3) perceived conversation impact; (4) practice norms. Changes in clinicians’ beliefs, attitudes, and behaviors in these domains supported values and goals conversations, including: shifting paradigms about serious illness communication from ‘end-of-life planning’ to ‘knowing and honoring what matters most to patients;’ improvements in psychological safety that empowered advanced practice clinicians, nurses and social workers to take expanded roles; experiencing benefits of earlier values and goals conversations; shifting from avoidant norms to integration norms in which earlier serious illness discussions became part of routine processes. Culture-related inhibitors included: beliefs that conversations are about dying or withdrawing care; attitudes that serious illness communication is the physician’s job; discomfort managing emotions; lack of reliable processes. Conclusions Aspects of clinical culture, such as paradigms about serious illness communication and inter-professional empowerment, are linked to successful adoption of serious illness communication. Further research is warranted to identify effective strategies to enhance clinical culture and drive clinician practice change.
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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 | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".