What really matters in end-of-life discussions? Perspectives of patients in hospital with serious illness and their families
Bibliographic record
Abstract
BACKGROUND: The guideline-recommended elements to include in discussions about goals of care with patients with serious illness are mostly based on expert opinion. We sought to identify which elements are most important to patients and their families. METHODS: We used a cross-sectional study design involving patients from 9 Canadian hospitals. We asked older adult patients with serious illness and their family members about the occurrence and importance of 11 guideline-recommended elements of goals-of-care discussions. In addition, we assessed concordance between prescribed goals of care and patient preferences, and we measured patient satisfaction with goals-of-care discussions using the Canadian Health Care Evaluation Project (CANHELP) questionnaire. RESULTS: Our study participants included 233 patients (mean age 81.2 yr) and 205 family members (mean age 60.2 yr). Participants reported that clinical teams had addressed individual elements of goals-of-care discussions infrequently (range 1.4%-31.7%). Patients and family members identified the same 5 elements as being the most important to address: preferences for care in the event of life-threatening illness, values, prognosis, fears or concerns, and questions about goals of care. Addressing more elements was associated with both greater concordance between patients' preferences and prescribed goals of care, and greater patient satisfaction. INTERPRETATION: We identified elements of goals-of-care discussions that are most important to older adult patients in hospital with serious illness and their family members. We found that guideline-recommended elements of goals-of-care discussions are not often addressed by health care providers. Our results can inform interventions to improve the determination of goals of care in the hospital setting.
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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 | 0.018 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".