Experiences of Canadian Oncologists with Difficult Patient Deaths and Coping Strategies Used
Bibliographic record
Abstract
OBJECTIVES: We aimed to explore and identify what makes patient death more emotionally difficult for oncologists and how oncologists cope with patient death. METHODS: A convenience sample of 98 Canadian oncologists (50 men, 48 women) completed an online survey that included a demographics section and a section about patient death. RESULTS: More than 80% of oncologists reported that patient age, long-term management of a patient, and unexpected disease outcomes contributed to difficult patient loss. Other factors included the doctor-patient relationship, identification with the patient, caregiver-related factors, oncologist-related factors, and "bad deaths." Oncologists reported varying strategies to cope with patient death. Most prevalent was peer support from colleagues, including nurses and other oncologists. Additional strategies included social support, exercise and meditation, faith, vacations, and use of alcohol and medications. CONCLUSIONS: Oncologists listed a number of interpersonal and structural factors that make patient death challenging for them to cope with. Oncologists reported a number of coping strategies in responding to patient death, including peer support, particularly from nursing colleagues. No single intervention will be suitable for all oncologists, and institutions wishing to help their staff cope with the emotional difficulty of patient loss should offer a variety of interventions to maximize the likelihood of oncologist participation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".