Nurses' knowledge and attitudes towards artificial intelligence and related factors: A systematic review
Bibliographic record
Abstract
This systematic review investigates nurses' knowledge, attitudes, and related factors concerning artificial intelligence (AI). A comprehensive and systematic search was conducted across various international electronic databases, including Scopus, PubMed, and Web of Science. Keywords were derived from Medical Subject Headings (MeSH) and included terms such as "knowledge", "attitude", "artificial intelligence", and "nurses". The search encompassed records from the earliest available to July 3, 2024. The quality of the studies included in this systematic review was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS tool), an appraisal instrument designed for cross-sectional studies. In total, 1,213 nurses were surveyed across five cross-sectional studies. 76.37% of these participants were female. The systematic review included studies conducted in Egypt (n=2), the United States (n=1), China (n=1), and Germany (n=1). The average knowledge score of AI among nurses in four studies was 21.93 out of 100, indicating poor knowledge of AI. There was a significant relationship between nurses' knowledge of AI and education level, gender, and the type of healthcare facility in which they work (n=1). The average positive attitude of nurses towards AI, as observed in three studies, was 67.19 out of 100, reflecting a generally high positive attitude among nurses. There was a significant relationship between nurses' education level and attitude toward AI (n=1). Therefore, policymakers and health managers can enhance nurses' knowledge and positive attitudes by focusing on factors such as education level, gender, and type of healthcare facility.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".