Knowledge, attitudes, practices, and barriers of artificial intelligence as predictors of intent to stay among nurses: A cross-sectional study
Abstract
Objective Integrating artificial intelligence (AI) in healthcare presents significant opportunities and challenges for nurses and other healthcare professionals. AI adoption may influence nurses’ work environment and overall healthcare. This study aimed to describe the level of knowledge, attitudes, practices, and barriers of AI among nurses in Jordan and describe their influence on nurses’ intent to stay in their job positions. Methods A descriptive correlational cross-sectional study was conducted among nurses working in governmental hospitals in Jordan. Data were collected using two validated instruments and were analyzed using descriptive statistics, Pearson correlation, and multivariate regression. Results The results showed that the mean scores of AI knowledge, attitudes, practices, barriers, and intent to stay were as follows: 3.91 (0.67), 4.15 (0.51), 3.98 (0.56), 3.93 (0.62), and 4.17 (0.49), respectively. While AI attitudes (r = .64, β = .34, P < .001) and practices (r = .58, β = .29, P < .001) significantly predicted intent to stay, barriers to AI were negatively correlated with it (r = −.42, β = −.14, P < .05). Conclusion A positive attitude and practical engagement with AI Could significantly enhance nurses’ intent to stay, while barriers undermine retention. Addressing these factors through targeted training and policy reforms is crucial for nursing workforce stability.
How strong is the evidence?
Evidence Hub reads this as a cross-sectional. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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