Clinician Perspectives on Caring for Dying Patients During the Pandemic
Bibliographic record
Abstract
BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has affected the hospital experience for patients, visitors, and staff. OBJECTIVE: To understand clinician perspectives on adaptations to end-of-life care for dying patients and their families during the pandemic. DESIGN: Mixed-methods embedded study. (ClinicalTrials.gov: NCT04602520). SETTING: 3 acute care medical units in a tertiary care hospital from 16 March to 1 July 2020. PARTICIPANTS: 45 dying patients, 45 family members, and 45 clinicians. INTERVENTION: During the pandemic, clinicians continued an existing practice of collating personal information about dying patients and "what matters most," eliciting wishes, and implementing acts of compassion. MEASUREMENTS: Themes from semistructured clinician interviews that were summarized with representative quotations. RESULTS: Many barriers to end-of-life care arose because of infection control practices that mandated visiting restrictions and personal protective equipment, with attendant practical and psychological consequences. During hospitalization, family visits inside or outside the patient's room were possible for 36 patients (80.0%); 13 patients (28.9%) had virtual visits with a relative or friend. At the time of death, 20 patients (44.4%) had a family member at the bedside. Clinicians endeavored to prevent unmarked deaths by adopting advocacy roles to "fill the gap" of absent family and by initiating new and established ways to connect patients and relatives. LIMITATION: Absence of clinician symptom or wellness metrics; a single-center design. CONCLUSION: Clinicians expressed their humanity through several intentional practices to preserve personalized, compassionate end-of-life care for dying hospitalized patients during the SARS-CoV-2 pandemic. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research and Canadian Critical Care Trials Group Research Coordinator Fund.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".