Study of Nurses’ Knowledge About COVID-19 in Al-Hilla Teaching Hospitals
Bibliographic record
Abstract
Objectives: The COVID-19 pandemic is a major health crisis that emerged at the end of the first quarter of the 21st century and changed the lives of millions of people globally. Nurses have close contact with infectious patients; therefore, nurses need to obtain sufficient knowledge in this regard. They need to assess their level of knowledge about COVID-19 and explore their needs to enhance and improve their knowledge in order to be more qualified to fight this virus. This study intends to assess nurses’ knowledge regarding COVID-19 and to find out the differences in nurses’ knowledge about COVID-19 with regard to their demographic variables. Methods: A descriptive (cross-sectional design) study was conducted to assess nurses’ knowledge regarding COVID-19 in Al-Hilla teaching hospitals in Al-Hilla City, Iraq, from September 25, 2020, to February 20, 2022. Using a convenient sampling method, a sample of 200 nurses working at epidemic hospitals was selected. The data collection process began with a questionnaire, which nurses answered as a self-report (questionnaire). Then the questionnaires were collected from the respondents. The average time to fill out the questionnaire was between 10 and 20 minutes. Results: The findings indicate that most nurses (58%) had poor knowledge about COVID-19. There were significant differences in nurses’ knowledge about COVID-19 with regard to their educational levels (P<0.05) and experience in epidemiological wards (P<0.05). Also, there was no difference between nurses’ attitudes toward COVID-19 with regard to their experience in epidemiological wards (P>0.05), but there was a significant difference between nurses’ knowledge about COVID-19 with regard to their source of information about COVID-19 (P<0.05). Discussion: The present study revealed that nurses had moderate knowledge about COVID-19. They had poor knowledge related to the prevention of COVID-19, while they had poor knowledge as overall knowledge related to COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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 teacher head, 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".