The social psychological impact of the COVID-19 pandemic on medical staff in China: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak required the significantly increased working time and intensity for health professionals in China, which may cause stress signs. METHODS: From March 2-13 of 2020, 4,618 health professionals in China were included in an anonymous, self-rated online survey regarding their concerns on exposure to the COVID-19 outbreak. The questionnaires consisted of five parts: basic demographic information and epidemiological exposure; occupational and psychological impact; concerns during the episode; coping strategies; and the Huaxi Emotional-Distress Index (HEI). RESULTS: About 24.2% of respondents experienced high levels of anxiety or/and depressive symptoms since the COVID-19 outbreak. Respondents who worried about their physical health and those who had COVID-19 infected friends or close relatives were more likely to have high HEI levels, than those without these characteristics. Further, family relationship was found to have an independent protective effect against high HEI levels. Their main concerns were that their families would not be cared for and that they would not be able to work properly. Compared to respondents with clear emotional problems, those with somewhat hidden emotional issues adopted more positive coping measures. CONCLUSIONS: About a quarter of medical staff experienced psychological problems during the pandemic of COVID-19. The psychological impact of stressful events was related to worrying about their physical health, having close COVID-19 infected acquaintances and family relationship issues. Therefore, the psychological supprot for medical staff fighting in the COVID-19 pandemic may be needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".