Achieving Goals of Care Decisions in Chronic Critical Illness
Bibliographic record
Abstract
BACKGROUND: Physicians, patients, and families alike perceive a need to improve how goals of care (GOC) decisions occur in chronic critical illness (CCI), but little is currently known about this decision-making process. RESEARCH QUESTION: How do intensivists from various health systems facilitate decision-making about GOC for patients with CCI? What are barriers to, and facilitators of, this decision-making process? STUDY DESIGN AND METHODS: We conducted semistructured interviews with a purposeful sample of intensivists from the United States and Canada using a mental models approach adapted from decision science. We analyzed transcripts inductively using qualitative description. RESULTS: We interviewed 29 intensivists from six institutions. Participants across all sites described GOC decision-making in CCI as a complex, longitudinal, and iterative process that involved substantial preparatory work, numerous stakeholders, and multiple family meetings. Intensivists required considerable time to collect information on prior events and conversations, and to arrive at a prognostic consensus with other involved physicians prior to meeting with families. Many intensivists stressed the importance of scheduling multiple family meetings to build trust and relationships prior to explicitly discussing GOC. Physician-identified barriers to GOC decision-making included 1-week staffing models, limited time and cognitive bandwidth, difficulty eliciting patient values, and interpersonal challenges with care team members or families. Potential facilitators included scheduled family meetings at regular intervals, greater interprofessional involvement in decisions, and consistent messaging from care team members. INTERPRETATION: Intensivists described a complex time- and labor-intensive group process to achieve GOC decision-making in CCI. System-level interventions that improve how information is shared between physicians and decrease logistical and relational barriers to timely and consistent communication are key to improving GOC decision-making in CCI.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".