Nurses' knowledge and attitude towards COVID-19 in the context of the acute health care settings in Jordan
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Purpose Nurses are at the front line in facing the COVID-19 outbreak and are at increased risk of becoming infected and might be the source of transmission in health-care facilities and the community. The purpose of this study is to assess the knowledge and attitude toward COVID1-19 among nurses in acute care settings in Jordan. This is expected to help with the global initiative to combat the COVID-19 epidemic. Design/methodology/approach A cross-sectional design was used to survey nurses' knowledge and attitude of COVID-19 among Jordanian nurses working in acute care settings. Findings The grand mean of knowledge items response was 8.94, implying that respondents possessed a high level of knowledge. The overall attitude score was positive for the participants, with a mean score of 5.93. Moreover, the results showed a significant relationship between knowledge and attitude scores. Originality/value The findings suggest that nurses in Jordan showed a high level of knowledge and a positive attitude toward COVID-19 during the outbreak's rapid rise period. This study showed specific aspects of knowledge and attitudes that should be focused on in future awareness and educational programs to promote all preventive and safety measures of COVID-19.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".