Physicians' Views on Advance Care Planning and End‐of‐Life Care Conversations
Bibliographic record
Abstract
OBJECTIVES: To evaluate physicians' views on advance care planning, goals of care, and end-of-life conversations. DESIGN: Random sample telephone survey. SETTING: United States. PARTICIPANTS: Physicians (primary care specialists; pulmonology, cardiology, oncology subspecialists) actively practicing medicine and regularly seeing patients aged 65 and older (N=736; 81% male, 75% white, 66% aged ≥50. MEASUREMENTS: A 37-item telephone survey constructed by a professional polling group with national expert oversight measured attitudes and perceptions of barriers and facilitators to advance care planning. Summative data are presented here. RESULTS: Ninety-nine percent of participants agreed that it is important to have end-of-life conversations, yet only 29% reported that they have formal training for such conversations. Those most likely to have training included younger physicians and those caring for a racially and ethnically diverse population. Patient values and preferences were the strongest motivating factors in having advance care planning conversations, with 92% of participants rating it extremely important. Ninety-five percent of participants reported that they supported a new Medicare fee-for-service benefit reimbursing advance care planning. The biggest barrier mentioned was time availability. Other barriers included not wanting a patient to give up hope and feeling uncomfortable. CONCLUSION: With more than half of physicians reporting that they feel educationally unprepared, there medical school curricula need to be strengthened to ensure readiness for end-of-life conversations. Clinician barriers need to be addressed to meet the needs of older adults and families. Policies that focus on payment for quality should be evaluated at regular intervals to monitor their effect on advance care planning.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".