Quality of life during the epidemic of COVID-19 and its associated factors among enterprise workers in East China
Bibliographic record
Abstract
Abstract Background: The impact of COVID-19 related quarantine on quality of life was expected and unclear for enterprise workers. We investigated the quality of life during the epidemic in enterprise workers just returned to work and assessed its potential influencing factors to get a better understanding of the impact of COVID-19 epidemic. Methods: This was a cross-sectional study among enterprise workers in Deqing and Taizhou, Zhejiang Province, China. Chinese version of EQ5D to assess life quality, and information about general characteristics and COVID-19 related information was collected by a structured questionnaire online distributed through the social application “Wechat”. Results: A total of 2435 participants were enrolled, 59.5% of which worked in Deqing. About 50% reported worries about the COVID-2019 epidemic and 40.1% of participants performed centralized or home quarantine during the epidemic. The mean EQ-5D score and VAS were 0.990 and 93.5. Multiple logistic regression suggested that physical activities (ORad=0.46) and keeping home ventilation (ORad=0.04) was related with life quality in Deqing, while for participants in Taizhou, wearing a mask when going out (ORad=0.35), keeping home ventilation (ORad=0.16), unmarried status (ORad=2.38) and experienced centralized or home quarantine (ORad=1.64) was related with quality of life.Conclusions: Enterprise workers in two areas with different risk of COVID-19 experienced different life quality during the epidemic of COVID-19. Physical activity, marital status, worry about epidemic of COVID-19, keeping home ventilation, wearing a mask and quarantine were related with quality of life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".