Patients, Caregivers, and Healthcare Providers’ Experiences with COVID Care and Recovery across the Care Continuum: A Qualitative Study
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, discharge timelines were accelerated and patients were moved across the continuum of care, from acute to post-acute care, to relieve the strain in health system capacity. This study aimed to investigate the COVID-19 care pathway from the perspective of patients, caregivers, and healthcare providers to understand their experiences with care and recovery within and across care settings. Methods: A qualitative descriptive study. Patients and their families from an inpatient COVID-19 unit and healthcare providers from an acute or rehabilitation COVID-19 unit were interviewed. Results: A total of 27 participants were interviewed. Three major themes were identified: 1) The perceived quality and pace of COVID-19 care improved from acute care to inpatient rehabilitation; 2) Care transitions were especially distressing; and 3) Recovery from COVID-19 stagnated in the community. Conclusion: Inpatient rehabilitation was viewed as higher quality due to the slower paced care. Care transitions were distressing for stakeholders and enhanced integration between acute and rehabilitation care were suggested to improve patient handover. A lack of rehabilitation access led to recovery stagnating for patients discharged to the community. Telerehab may improve the transition to home and ensure access to adequate rehabilitation and support in the community.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".