The experiences of patients, family caregivers, healthcare providers, and health service leaders with compassionate care following hospitalization with COVID-19: a qualitative study
Bibliographic record
Abstract
PURPOSE: This study explored the experiences of patients, caregivers, healthcare providers, and health service leaders of compassion in the care of people hospitalized with COVID-19. MATERIALS AND METHODS: = 12). Primary research data were analyzed deductively under the "lens" of compassion, as defined by Goetz. RESULTS: Four interacting themes were found: (1) COVID-19 - to care or not to care? The importance of feeling safe, (2) A lonely illness - suffering in isolation with COVID-19, (3) Compassionate care for people with COVID-19 across the hospital continuum, and (4) Sustaining compassionate care for people hospitalized with COVID-19 - healthcare provider compassion fatigue and burnout. CONCLUSIONS: Compassionate care is not a given for people hospitalized with COVID-19. Healthcare providers must feel safe to provide care before responding compassionately. People hospitalized with COVID-19 experience additional suffering through isolation. Compassionate care for people hospitalized with COVID-19 is more readily identifiable in the rehabilitation setting. However, compassion fatigue and burnout in this context threaten healthcare sustainability.IMPLICATIONS FOR REHABILITATIONHealthcare providers need to feel physically and psychologically safe to provide compassionate care for people hospitalized with COVID-19.People hospitalized with COVID-19 infection experience added suffering through the socially isolating effects of physical distancing.Compassion and virtuous behaviours displayed by healthcare providers are expected and valued by patients and caregivers, including during the COVID-19 pandemic.High levels of compassion fatigue and burnout threaten the sustainability of hospital-based care for people with COVID-19.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".