Psychological Impact of the COVID-19 Pandemic on Chinese Health Care Workers: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The outbreak of COVID-19 has dominated headlines worldwide. The number of infections has continued to rise and had reached 30,000 worldwide at the time this paper was written. Because of the high risk of nosocomial transmission, medical health care workers may be experiencing substantial psychological stress. This descriptive study aimed to identify psychosocial effects on hospital staff associated with working in a hospital environment during the COVID-19 outbreak. OBJECTIVE: Our survey participants included 57 frontline clinicians working at Wuhan First Hospital and 157 medical students working at Jiangsu Provincial People's Hospital during the COVID-19 outbreak. The questionnaire we adopted included questions regarding the participants' personal well-being, sociodemographic characteristics, and psychological status. METHODS: 57 frontline clinicians working in Wuhan First Hospital and 157 medical training students working in Jiangsu Provincial Peoples Hospital during this outbreak participated in our survey. The questionnaire we adopted included questions regarding the participants' personal well-being, sociodemographic characteristics and the psychological status. RESULTS: The COVID-19 outbreak had psychological impacts both on formal workers and medical students. The psychological effects included sleep disorders, anxiety, and depression. There was no significant difference between the group of formal workers and medical students (P=.85), and more than 50% (30/54, 56%, vs. 83/157, 52.9%) of the respondents reported pandemic-related mental disorders. CONCLUSIONS: Our study indicates that the high risk of SARS-CoV-2 exposure caused substantial psychological stress among health care workers. This finding emphasizes the need to promote psychological crisis intervention for medical personnel during this epidemic.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".