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Nursing students are the primary reserve force for hospital nurses. With the shrinking of nurse human resources and the increase in turnover rates, understanding the job preferences of nursing students is crucial for attracting nursing students.
To systematically review published studies on discrete choice experiments involving nursing students.
Ten databases were systematically searched from their inception to January 15, 2025. Two researchers independently used the International Society for Pharmacoeconomics and Outcomes Research checklist to evaluate the quality of the included studies. Thematic analysis was used to classify the attributes into broad categories and corresponding subcategories. The frequency, significance, relative importance, and willingness-to-pay of each attribute in the included studies were analyzed.
Fifteen studies spanning 12 countries were included, with a total of 102 individual attributes extracted and divided into two broad categories and six subcategories. Non-financial attributes were the most frequently reported broad category. The subgroup analyses indicated that nursing students from high-income countries valued income and were highly concerned about the working atmosphere.
Linking Evidence to Action:
The results of this systematic review provide important evidence for developing incentive policies to attract nursing students to the nursing profession.
The utilisation of artificial intelligence in the context of nursing education has become increasingly extensive. However, various studies show differing perspectives and attitudes among nursing students, and the findings have not been systematically synthesised.
To systematically review the perceptions and attitudes of nursing students on the application of artificial intelligence in nursing education.
Mixed-methods systematic review.
A comprehensive literature search was conducted across 10 databases, including PubMed, Cochrane, Embase, Web of Science, CINAHL, Scopus, China Science and Technology Journal Database, SinoMed, China National Knowledge Internet, and WanFang database, the inclusive years of articles searched were from 1969 to 2025. Two researchers independently screened the literature and extracted the data. The mixed methods assessment tool was used to evaluate the risk of bias in the included literature. The relevant data were extracted and synthesised according to the Joanna Briggs Institute's convergence synthesis method, ensuring the comprehensive integration of qualitative and quantitative results. These results were then integrated into the Technology Acceptance Model.
A total of 28 articles were included, including 13 qualitative studies, 13 quantitative studies, and 2 mixed-method studies. According to the Technology Acceptance Model, the perceptions and attitudes of nursing students on the nursing education's adoption of artificial intelligence were integrated into 10 categories of three comprehensive themes: (i) Nursing students' perceptions and attitudes of the ease of use of artificial intelligence in nursing education, including 3 categories; (ii) nursing students' perceptions and attitudes on the usefulness of artificial intelligence in nursing education, including 4 categories; (iii) nursing students' behavioural intention, including 3 categories.
Overall, our study demonstrated that nursing students had an active willingness to utilise artificial intelligence. However, they acknowledged that certain issues persist regarding the ease and practicality of artificial intelligence in nursing education.
No patients or members of the public were directly involved in this systematic review, as the study synthesised existing literature.
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