Bereavement Practices of Physicians in Oncology and Palliative Care
Bibliographic record
Abstract
BACKGROUND: Cancer physicians frequently interact with dying patients, but little is known about these physicians' practices. The purpose of this study was to evaluate the frequency and nature of bereavement practices among medical oncologists (MOs), radiation oncologists (ROs), and palliative care specialists (PCs); and to identify factors associated with bereavement follow-up. METHODS: Survey of all Canadian MOs, ROs, and PCs via their respective national organizations using an anonymous electronic and postal mail survey. RESULTS: A total of 535 of 756 eligible physicians completed the survey (71%). Overall, 33.3% (95% confidence interval [CI], 29.3%-37.4%) of respondents indicated that they usually or always make a telephone call, send a condolence card, or attend a funeral following a patient's death; 30.5% (95% CI, 26.5%-34.4%) reported performing at least 1 of these practices sometimes; and 36.2% (95% CI, 32.1%-40.3%) reported performing at least 1 of these practices rarely or never. Among the specific practices, respondents were more likely to call a family at least sometimes than to send a condolence card or attend funeral services. Palliative care specialists reported the highest rates of bereavement follow-up. In multivariate regression analysis, female sex, working in an academic setting, palliative care specialty, lack of formal palliative care program, endorsement of the statement that physicians had a responsibility to send a condolence card, and high number of patient deaths were associated with more frequent bereavement follow-up. CONCLUSIONS: Few cancer physicians provide bereavement follow-up routinely. This suggests that consensus is lacking among cancer physicians regarding their role in bereavement 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 | 0.001 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".