Educating Medical Residents in End-of-Life Care: Insights from a Multicenter Survey
Bibliographic record
Abstract
BACKGROUND: Physicians play a key role in the provision of quality end-of-life (EOL) care but often lack requisite knowledge and skills. Residency programs must ensure training in palliative/EOL care to address this gap. OBJECTIVE: To guide the development of curricula, we assessed internal medicine residents' attitudes, knowledge, perceived competence, and learning priorities in EOL care. DESIGN: Cross-sectional, self-administered, descriptive survey using a convenience sample. SUBJECTS: Internal medicine residents at five universities across Canada. RESULTS: Of a total of 318 internal medicine residents, 185 (58%) participated in the survey. The majority (81.7%) agreed learning from dying patients was meaningful although 48.1% felt guilty, and 40.6% a failure at least sometimes after a patient's death. Two thirds had provided care to more than 10 dying patients. Most (73%) had conducted at least 3 family meetings; 26.7% were never observed. Mean self-assessed preparedness to provide EOL care was 6.1 +/- 2 (scale 0-10) and mean comfort level 3.2 +/- 0.8 (scale 0-5). Residents reported more than average competence in 50% of EOL competencies listed with record keeping highest (3.6 +/- 0.7) and use of nonpharmacologic interventions for pain lowest (2.2 +/- 0.8). Priority for learning was rated above average for all EOL competencies listed with use of opioids for management of pain highest (4.1 +/- 0.9) and discussing euthanasia lowest (3.1 +/- 1.3). CONCLUSIONS: Internal medicine residents value opportunities to learn from dying patients but often lack supervision and experience emotional distress. Comparing residents' attitudes, perceptions of competence, and learning priorities provide insights into why certain EOL competencies are more challenging to teach and can guide development of meaningful educational experiences.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".