Nurses’ willingness to work with COVID‐19 patients: The role of knowledge and attitude
Bibliographic record
Abstract
AIM: This study aims to assess the role of nurses' knowledge and attitude in relation to their willingness to work with patients diagnosed with COVID-19 in Qatar. DESIGN: A cross-sectional study. METHODS: A self-administered, 35-item online survey was circulated to the Registered Nurses working in Hamad Medical Corporation, the principal healthcare provider in Qatar. RESULTS: A total of 580 attempts to complete the survey. Of them, 377 completed surveys with a response rate of 65%. Logistic regression was used to predict nurses' willingness to work with patients with COVID-19. Nurses' knowledge level and monetary compensation that is associated with the work-environment risk category were found to have a significant positive relationship with the nurses' willingness to care for patients with COVID-19 (p < .05). The findings of this study may help nursing leaders design educational programmes and remuneration models that may help boost nurses' willingness to work with high-risk patient groups, especially during a pandemic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".