Psychological responses among nurses caring for patients with COVID-19: a comparative study in China
Bibliographic record
Abstract
Frontline healthcare nurses devoted themselves to deal with the outbreak of COVID-19, saving many lives. However, they are under incredible unknown psychological pressures with a considerable risk of infection. In this study, a self-administered questionnaire was used to survey 593 frontline nurses in Wuhan City and non-Hubei provinces for psychological responses from March 1 to March 10, 2020. Compared with nurses outside Hubei Province, those working in Wuhan were more likely to feel physically and mentally exhausted. Their probable depression and anxiety were significantly higher than those of nurses outside Hubei province (31.2%, 18.3% vs. 13.8%, 5.9%). Correspondingly, the depressive symptoms were more often reported in the Wuhan group (70.8% vs. 41.4%). Although Wuhan received wishes, concerns, and abundant psychological and material resources from all of the world, the survey-based study found that frontline nurses in Wuhan still had higher depression and anxiety with less social support compared with nurses from non-Hubei provinces. Unexpectedly, only 4.0% of nurses have sought psychological assistance. These findings suggested that the short-term psychological impact of frontline nurses in Wuhan during the COVID-19 outbreak was extremely high compared with nurses outside Hubei Province. This research enlightened the efficient integration of psychological resources, the optimization of the nurse emergency psychological assistance system, and the mental health care of medical staff during the outbreak of epidemics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".