Experiences of COVID-19 patients in a Fangcang shelter hospital in China during the first wave of the COVID-19 pandemic: a qualitative descriptive study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine COVID-19 patients' experiences in a Fangcang shelter hospital in China, to provide insights into the effectiveness of this centralised isolation strategy as a novel solution to patient management during emerging infectious disease outbreaks. DESIGN: This study adopted a qualitative descriptive design. Data were collected by individual semistructured interviews and analysed using thematic analysis. SETTING: This study was undertaken in 1 of the 16 Fangcang shelter hospitals in Wuhan, China between 28 February 2020 and 7 March 2020. Fangcang shelter hospitals were temporary healthcare facilities intended for large-scale centralised isolation, treatment and disease monitoring of mild-to-moderate COVID-19 cases. These hospitals were an essential component of China's response to the first wave of the COVID-19 pandemic. PARTICIPANTS: A total of 27 COVID-19 patients were recruited by purposive sampling. Eligible participants were (1) COVID-19 patients; (2) above 18 years of age and (3) able to communicate effectively. Exclusion criteria were (1) being clinically or emotionally unstable and (2) experiencing communication difficulties. RESULTS: Three themes and nine subthemes were identified. First, COVID-19 patients experienced a range of psychological reactions during hospitalisation, including fear, uncertainty, helplessness and concerns. Second, there were positive and negative experiences associated with communal living. While COVID-19 patients' evaluation of essential services in the hospital was overall positive, privacy and hygiene issues were highlighted as stressors during their hospital stay. Third, positive peer support and a trusting patient-healthcare professional relationship served as a birthplace for resilience, trust and gratitude in COVID-19 patients. CONCLUSIONS: Our findings suggest that, while sacrificing privacy, centralised isolation has the potential to mitigate negative psychological impacts of social isolation in COVID-19 patients by promoting meaningful peer connections, companionship and support within the shared living space. To our knowledge, this is the first study bringing patients' perspectives into healthcare service appraisal in emergency shelter hospitals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".