Effect of COVID-19 stressors on healthcare workers’ performance and attitude at Suez Canal university hospitals
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 is an emerging respiratory disease caused by a novel coronavirus effect on 10-20% of total healthcare workers and was first detected in December 2019 in Wuhan, China. This study was designed to assess effect of COVID-19 stressors on healthcare workers’ performance and attitude. A descriptive cross sectional research design was used. A convenient sample (all available healthcare workers) physicians “112,”, nurses “183,” pharmacists “31,” and laboratory technicians “38” was participated to conduct aim of the study. Utilize the study with two tools; online self-administrated questionnaire to assess level of knowledge, attitude, and infection control measures regarding coronavirus disease 2019 and COVID-19 stress scales to assess the varied stressors among healthcare workers. Results More than three quarter of the studied participants had satisfactory level of knowledge and infection control measures. Approximately all of the studied participants had positive attitude regarding COVID-19. A total of 57.4% of the studied medical participants had moderate COVID-19 psychological stress levels, while 49.1% of the studied paramedical participants had moderate COVID-19 psychological stress levels. But less than one quarter had severe COVID-19 psychological stress levels. There is a significant correlation between COVID-19 psychological stressor levels and satisfactory level of knowledge among medical participants. Conclusion/implications for practice Most of healthcare workers had satisfactory level of knowledge, infection control measures, and positive attitude regarding COVID-19. Most of them had moderate COVID-19 psychological stress levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".