Doctors' emotional reactions to recent death of a patient: cross sectional study of hospital doctors
Bibliographic record
Abstract
OBJECTIVES: To describe doctors' emotional reactions to the recent death of an "average" patient and to explore the effects of level of training on doctors' reactions. DESIGN: Cross sectional study using quantitative and qualitative data. SETTING: Two academic teaching hospitals in the United States. PARTICIPANTS: 188 doctors (attending physicians (equivalent to UK consultants), residents (equivalent to UK senior house officers), and interns (equivalent to UK junior house officers)) who cared for 68 patients who died in the hospital. MAIN OUTCOME MEASURES: Doctors' experiences in providing care, their emotional reactions to the patient's death, and their use of coping and social resources to manage their emotions. RESULTS: Most doctors (139/188, 74%) reported satisfying experiences in caring for a dying patient. Doctors reported moderate levels of emotional impact (mean 4.7 (SD 2.4) on a 0-10 scale) from the death. Women and those doctors who had cared for the patient for a longer time experienced stronger emotional reactions. Level of training was not related to emotional reactions, but interns reported needing significantly more emotional support than attending physicians. Although most junior doctors discussed the patient's death with an attending physician, less than a quarter of interns and residents found senior teaching staff (attending physicians) to be the most helpful source of support. CONCLUSIONS: Doctors who spend a longer time caring for their patients get to know them better but this also makes them more vulnerable to feelings of loss when these patients die. Medical teams may benefit from debriefing within the department to give junior doctors an opportunity to share emotional responses and reflect on the patient's death.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".