Bibliographic record
Abstract
BACKGROUND: A growing body of research indicates that physicians suffer high levels of stress, depression and burnout. Related literature has found that physician stress can negatively impact patient care. This study builds upon previous research that found some dying patients experienced 'iatrogenic suffering' caused by the way physicians communicated with them regarding terminal diagnoses and palliative treatment. The goal of this research was to explore physicians' experiences of dealing with patient death in order to understand how such experiences affect them and their communication with patients. METHODS: This study used qualitative methods to conduct and analyse 10 individual, semistructured interviews with senior physicians from several specialty areas at a large, tertiary care hospital. The resulting themes were validated using member checks and expert review. RESULTS: This article presents five essential themes that provide a concise description of the lived experience of patient death for these physicians. INTERPRETATION: These themes indicate that physicians can experience very strong and lasting emotional reactions to some patient deaths, and also that patient death can elicit intense experiences related to professional responsibility and competence. A key finding is the description of a complex process of managing the balance between personal and professional reactions in the face of patient death. The implication is that difficulties negotiating this balance may lead to unintended lapses in compassion and suboptimal outcomes in patient care.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".