Mental health in COVID-19 Delta variant survivors and healthcare workers during the 2021 outbreak in Nanjing, China: a cross-sectional study
Bibliographic record
Abstract
Since the coronavirus disease 2019 (COVID-19) Delta variant was discovered in India in October 2020, it has rapidly triggered a second outbreak globally. However, the effects of the COVID-19 Delta variant on mental health in survivors and healthcare workers are unclear. The aim of this study is to assess mental health among the COVID-19 Delta variant survivors and healthcare workers, and analyze the possible impact factors. This survey-based, cross-sectional study used the Symptom Check List-90 Revised questionnaire to evaluate psychological status among 60 COVID-19 Delta variant survivors, 162 nurses, and 72 hygienists in Nanjing, China. Three indices and nine dimensions were compared for job, education level, gender, age, and marriage classification. Data were analyzed using SPSS 25.0. Mental distress among participants was not very serious in general. The survivors presented the highest score, followed by the hygienists, and the lowest score was in nurses. Low-educated individuals and women showed significant increase. No significant difference was noted in age and marriage classification. In this survey study of COVID-19 Delta variant survivors and healthcare workers in Nanjing, China, the survivors needed psychological support immediately. Meanwhile, healthcare workers warranted more attention, especially the lower education levels and women. A comprehensive emergency response plan was warranted.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".