On the frontlines in Shanghai: Stress, burnout and perceived benefit among COVID-19 testers and other personnel during the Omicron wave lockdown
Bibliographic record
Abstract
Abstract Background COVID-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 healthcare providers (HCP) and other support workers. A Mandarin self-report survey was administered via Wenjuanxing between April-June 2022 during the omicron-wave lockdown. The Perceived Stress Scale (PSS) and Maslach Burnout Inventory were administered, as well as pandemic-specific questions. Results 887 workers participated, of which 691 (77.9%) were HCPs. They were working a mean of 6.25±1.24 days/week for 9.77±4.28 hours/day. Most participants were burnt-out, with 143(16.1%) moderately and 98(11.0%) seriously. Total PSS was 26.85±9.92/56, with 353(39.8%) participants having elevated stress. Workers perceived their families primarily as fully supportive (n=610, 68.8%), or also extremely concerned ( n =203, 22.9%). Most wanted counselling and stress relief, but half( n =430) reported no time for it; indeed, 2/3rds wanted a few days off to rest ( n =601).Many workers perceived benefits: that they fostered more cohesive relationships ( n =581, 65.5%), they will be more resilient ( n =693, 78.1%), and were honored to serve ( n =747, 84.2%).Negative impacts were greater in HCPs, those with economic insecurity, and that did not perceive benefit (all p <.05).In adjusted analyses, those perceiving benefits showed significantly less burnout (OR=0.573, 95% CI=0.411 - 0.799), among other correlates. Conclusions Pandemic work, including among non-HCP, is 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".