Attitudes and perceptions of ICU nurses in caring for COVID-19 patients
Bibliographic record
Abstract
ICU Nurses’ attitudes and Perceptions towards COVID-19 Patients is crucial because it illuminates challenges regarding caring these patients in the critically ill. This cross-sectional descriptive correlational study employed 85 Saudi ICU nurses worked with critically ill COVID-19 patients since 2020 until present in the government hospitals affiliated in the Ministry of Health (MOH) in Hail Region. These hospitals included King Khalid Hospital, King Salman Specialist Hospital, Hail general Hospital, Maternity and Child Hospital, and Sharaf Hospital, as they all have intensive care units in different specialties. A self-administered questionnaire through online survey was used and composed of three parts, (1) socio-demographic profile of the respondents, (2) the attitudes of the respondents in Caring for COVID-19 Patients and (3) perceptions of the respondents in Caring for COVID-19 Patients. The tool was adopted from Al-Dossary et al. (2020). The majority of nurses participated in the study aged between 25-34 years old, hold bachelor’s degree, and had between 2-5 years clinical experience. Nurses’ perception and attitude were moderately positive. However, male and females were differed in respect to attitude and perception. Likewise, education, age, and length of hospital experience were also influential to the attitudes and perception. In conclusion, healthcare organizations should evaluate ICU nurses attitudes and perception of pandemics to ensure safer and optimal practice.
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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 | 0.006 |
| 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) | 0.001 | 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".