On the Frontlines in Shanghai
Bibliographic record
Abstract
BACKGROUND: COVID-19 (COronaVIrus Disease-19) control measure stringency, including testing, has been among the highest globally in China. Psychosocial impact on pandemic workers in Shanghai and their pandemic-related attitudes were investigated. METHODS: Participants in this cross-sectional study were health care providers (HCPs) and other pandemic workers. A Mandarin online survey was administered between April and June 2022 during the omicron-wave lockdown. The Perceived Stress Scale and Maslach Burnout Inventory were administered. RESULTS: Eight hundred eighty-seven workers participated, of which 691 (77.9%) were HCPs. They were working 6.25 ± 1.24 days per week for 9.77 ± 4.28 hours per day. Most participants were burned out, with 143 (16.1%) moderately and 98 (11.0%) seriously. The Perceived Stress Scale score was 26.85 ± 9.92 of 56, with 353 participants (39.8%) having elevated stress. Many workers perceived benefits: cohesive relationships (n = 581 [65.5%]), resilience (n = 693 [78.1%]), and honor (n = 747 [84.2%]). In adjusted analyses, those perceiving benefits showed significantly less burnout (odds ratio, 0.573; 95% confidence interval, 0.411 to 0.799), among other correlates. CONCLUSIONS: Pandemic work, including among non-HCPs, is highly stressful, but some can derive benefits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".