A Qualitative Study of Oncologists' Approaches to End-of-Life Care
Bibliographic record
Abstract
PURPOSE: To understand how oncologists provide care at the end of life, the emotions they experience in the provision of this care, and how caring for dying patients may impact job satisfaction and burnout. PARTICIPANTS AND METHODS: A face-to-face survey and in-depth semistructured interview of 18 academic oncologists who were asked to describe the most recent inpatient death on the medical oncology service. Physicians were asked to describe the details of the patient death, their involvement with the care of the patient, the types and sequence of their emotional reactions, and their methods of coping. Grounded theory qualitative methods were utilized in the analysis of the transcripts. RESULTS: Physicians, who viewed their physician role as encompassing both biomedical and psychosocial aspects of care, reported a clear method of communication about end-of-life (EOL) care, and an ability to positively influence patient and family coping with and acceptance of the dying process. These physicians described communication as a process, made recommendations to the patient using an individualized approach, and viewed the provision of effective EOL care as very satisfying. In contrast, participants who described primarily a biomedical role reported a more distant relationship with the patient, a sense of failure at not being able to alter the course of the disease, and an absence of collegial support. In their descriptions of communication encounters with patients and families, these physicians did not seem to feel they could impact patients' coping with and acceptance of death and made few recommendations about EOL treatment options. CONCLUSION: Physicians' who viewed EOL care as an important role described communicating with dying patients as a process and reported increased job satisfaction. Further research is necessary to determine if educational interventions to improve physician EOL communication skills could improve physician job satisfaction and decrease burnout.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".