Identifying barriers and facilitators to palliative care integration in the management of hospitalized patients with COVID-19: A qualitative study
Bibliographic record
Abstract
Background: Palliative care is well suited to support patients hospitalized with COVID-19, but integration into care has been variable and generally poor. Aim: To understand barriers and facilitators of palliative care integration for hospitalized patients with COVID-19. Methods: Internists, Intensivists and palliative care physicians completed semi-structured interviews about their experiences providing care to patients with COVID-19. Results were analysed using thematic analysis. Results: Twenty-three physicians (13 specialist palliative care, five intensivists, five general internists) were interviewed; mean ± SD age was 42 ± 11 years and 61% were female. Six thematic categories were described including: patient and family factors, palliative care knowledge, primary provider factors, COVID-19 specific factors, palliative care service factors, and leadership and culture factors. Patient and family factors included patient prognosis, characteristics that implied prognosis (i.e., age, etc.), and goals of care. Palliative care knowledge included confidence in primary palliative care skills, misperception that COVID-19 is not a ‘palliative diagnosis’, and the need to choose quantity or quality of life in COVID-19 management. Primary provider factors included available time, attitude, and reimbursement. COVID-19 specific factors were COVID-19 as an impetus to act, uncertain illness trajectory, treatments and outcomes, and infection control measures. Palliative care service factors were accessibility, adaptability, and previous successful relationships. Leadership and culture factors included government-mandated support, presence at COVID planning tables, and institutional and unit culture. Conclusion: The study findings highlight the need for leadership support for formal integrated models of palliative care for patients with COVID-19, a palliative care role in pandemic planning, and educational initiatives with primary palliative care providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".