Psychological Problems and Fear of COVID-19 Pandemic among Nurses and General Population: A Comparative Study
Bibliographic record
Abstract
COVID-19 is a source of stress with incredible impact, both for people and socialpublic gatherings. Various people may experience various levels of psychologicalemergency, particularly those at the center of the incident. This study aimed tocompare the level of psychological problems (depression, anxiety, and stress) andfear among nurses and general population as a result of COVID -19 pandemic. Across-sectional study design was utilized in this study. A probability sample of 132nurses working in Sharkia Governorate hospitals and 268 individuals from generalpopulation participated in this study by using an anonymous online questionnaire.Three tools were completed by the participants in this study were a sociodemographic data sheet, the Depression, Anxiety, and Stress Scale-21, and fear ofCoronaVirus-19 Scale. Results revealed that nearly one quarter of the studiednurses' group had moderate level of depression, anxiety and stress. However, morethan one quarter of the studied general population had extremely severe level ofdepression and anxiety as well as severe level of stress. Severe level of fear ofCOVID-19 was experienced among one quarter of participants from nurses andgeneral population. The study concluded that was statistically significant positivecorrelations were found between fear of COVID-19, depression, stress, and anxietyin both groups. Therefore, it is recommended to develop and implementpsychological interventions for improving mental health and psychologicalresilience during the pandemic COVID-19 of both nurses and general population.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".