Differences in Palliative Care Provision by Primary and Specialist Providers Supporting Patients With COVID-19: A Qualitative Study
Bibliographic record
Abstract
Objectives:To describe the delivery of palliative care by primary providers (PP) and specialist providers (SP) to hospitalized patients with COVID-19. Methods:PP and SP completed interviews about their experiences providing palliative care. Results were analyzed using thematic analysis. Results:Twenty-one physicians (11 SP, 10 PP) were interviewed. Six thematic categories emerged. Care provision: PP and SP described their support of care discussions, symptom management, managing end of life, and care withdrawal. Patients provided care: PP described patients at end of life, with comfort-focused goals; SP included patients seeking life-prolonging treatments. Approach to symptom management: SP described comfort, and PP discomfort in providing opioids with survival-focused goals. Goals of care: SP felt these conversations were code status-focused. Supporting family: both groups indicated difficulties engaging families due to visitor restrictions; SP also outlined challenges in managing family grief and need to advocate for family at the bedside. Care coordination: internist PP and SP described difficulties supporting those leaving the hospital. Conclusion:PP and SP may have a different approach to care, which may affect consistency and quality of 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".