CLINICIAN EXPRESSIONS OF CONDOLENCE AFTER THE DEATH OF A PATIENT: AN INTERNATIONAL SURVEY
Bibliographic record
Abstract
Abstract There is little literature about expressions of condolence from providers to family members of those that have died, and no known literature reported during the COVID-19 pandemic. This study utilized an investigator-developed survey of healthcare clinicians about contacting families of patients who have died. It conducted via email and online from October to December 2021. Of 131 respondents, 67% were female, from 23 states, Canada, and UK. Half (49%) had >15 years experience, and most (85%) were attending physicians. The majority (99%) reported that a patient had died in their care within last year, while 18% reported lost >10 patients per month. Methods of condolences were phone calls, and personal letters. Most (67%) reported no change in contacting families, while 23% increased. When asked if they would be interested in education on expressing condolences, 47% responded “yes.” Barriers to condolences were time (64%), unsure of what to say (20%), afraid family will be upset (19%), medical/legal reasons (10%), no training (8%), and no personal relationship (7%). Most, (85%) reported that most calls “went well or better than expected.” Females (vs. male) reported often/always sending letters (45% vs 20%, p=0.13), and often/always calling by phone significantly more (71% vs 63%, p=0.04). Younger (< 40) clinicians (vs. older) reported being very/moderately comfortable talking to families (72% vs 78%, p=0.79), phone calls (64% vs 69%, p=0.68) and personal letters (27% vs 42%, p=0.91). Nearly half of respondents requested training. This is a practice that should be further studied for clinician and family experience purposes.
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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 | 0.003 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".