Residents?? End-of-Life Decision Making with Adult Hospitalized Patients: A Review of the Literature
Bibliographic record
Abstract
PURPOSE: The authors performed a structured literature review to understand residents' experiences with end-of-life (EOL) decision making with adult hospitalized patients, specifically regarding decisions to withhold or withdraw advanced life-support measures. METHOD: An Ovid-based strategy was used to search Medline, ERIC, PsychINFO, and CINHAL databases for articles published between 1966 and February 2005, combining the domains of "resuscitation orders," "decision making," and "internship and residency." All quantitative and qualitative studies examining residents' EOL decision making with adult hospitalized patients were included. The authors developed and applied a scoring system for relevance and quality, performed data abstraction and quality assessment independently and in duplicate, then met to collate findings and identify factors in residents' EOL decision making. RESULTS: The searches yielded 884 articles, of which 26 were included. Variable methodologies precluded meta-analysis. In these studies, residents felt unprepared to handle patient EOL decision making, although exposure to EOL discussions helped them gain confidence. Residents' attitudes, skills, and knowledge were key determinants of whether EOL decisions were addressed. Many misinterpreted the terms "DNR" and "futility." Residents' understanding of the patient EOL decision-making process could be extremely variable, and their do-not-resuscitate discussions suboptimal. Residents' lived practice experience of the patient EOL decision-making process was often at odds with what they were taught in formal curricula. CONCLUSIONS: Educational strategies aimed at changing residents' knowledge, skills and attitude should address the hidden curriculum for the patient EOL decision-making process that is part of the experienced culture of every day practice. Future studies of this experienced culture would inform specific educational interventions.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".