Fighting COVID-19: a qualitative study into the lives of intensive care unit survivors in Wuhan, China
Bibliographic record
Abstract
OBJECTIVES: We aimed to provide an insight into the life of survivors of critical COVID-19 in China. METHODS: We conducted an online survey and qualitative interviews among intensive care unit survivors of critical COVID-19 between November and December 2020 in Wuhan, China. Eligible participants were asked to complete the EQ-5D-5L and the Short Form 36-Item Survey, and invited to participate in a semistructured face-to-face interview. Descriptive analyses and phenomenological approach were adopted to analyse quantitative and qualitative data, respectively. RESULTS: Of 10 survivors who completed the questionnaire, 8 participated in the interview. The mean scores±SD of EuroQol-5 Dimensions-5 Level utility and EuroQol-Visual Analogue Scale were 0.88±0.15 and 80.9±14.2, respectively. The qualitative interview identified four themes, namely poor physical health, post-traumatic stress, social stigma and family support. CONCLUSIONS: COVID-19 survivors continue fighting physical and psychological impacts. Despite strong family support, these patients are struggling with social stigma. It is a long, challenging journey to recovery for patients and society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.001 | 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".