Aug 2026· Nursing and Health Sciences· Vol 28 3, pp.
e70402
· 0 citations· 23 references
Medicine
TL;DR
The K-MAIRS can be a valid and reliable instrument for assessing artificial intelligence readiness among Korean nursing students and supports global initiatives to incorporate artificial intelligence competencies into nursing education using culturally tailored assessment tools.
Abstract
This study aimed to translate and culturally adapt the Medical Artificial Intelligence Readiness Scale for Medical Students into Korean and to examine its validity and reliability among nursing students. The scale designed in this methodological and cross-sectional study was translated and adapted using standard procedures and validated with 317 nursing students in South Korea. Experts reviewed the content validity. Structural validity was examined through exploratory and confirmatory factor analyses. Construct validity was evaluated through hypothesis testing using an external criterion measure. Reliability was evaluated using Cronbach's alpha and the split-half method. The final Korean version of the Medical Artificial Intelligence Readiness Scale (K-MAIRS) comprised 15 items across four factors-cognition, ability, vision, and ethics-explaining 66.88% of the variance. The K-MAIRS can be a valid and reliable instrument for assessing artificial intelligence readiness among Korean nursing students. This instrument supports global initiatives to incorporate artificial intelligence competencies into nursing education using culturally tailored assessment tools. It also supports educators and institutions in designing targeted strategies for artificial intelligence education within nursing curricula that enhance nurses' preparedness in artificial intelligence-driven healthcare environments.
The Turkish version of the FtAIS is a valid and reliable instrument for assessing fear of AI among nursing students and demonstrates acceptable content and construct validity and good internal consistency.
Şebnem Bilgiç, İdi̇l Şahi̇n· Journal of Nursing Measureme...· 0 citations
Aims To translate, revise and evaluate the Chinese‐version Nursing Leaders’ Readiness for Artificial Intelligence Scale and assess Chinese nursing leaders’ AI readiness. Design Cross‐sectional survey. Methods The research team conducted translation, cognitive interviews, a pilot survey and psychometric evaluation. Survey participants were 762 nursing managers. The reliability and validity of the Chinese‐version scale were examined. The status and influencing factors of AI readiness among Chinese nursing managers were investigated. Results The translated scale comprises 20 items. Cronbach’s α was 0.824. Regarding validity, the three‐factor model demonstrated a good fit. The square roots of the average variance extracted, absolute values of correlation coefficients among dimensions and content validity index values were acceptable. The mean AI readiness score was 65.27 ± 9.78. Of the participants, 624 scored above 60 points, 136 scored 40–60 points and 2 scored below 40 points. Scores differed significantly by hospital type, hospital level, department, training participation, prior AI tool or system use and frequency of AI usage. Conclusion The Chinese‐version scale demonstrated good reliability and validity and can effectively assess AI readiness among Chinese nursing managers. Nursing leaders generally demonstrated a favourable level of AI readiness; however, factors such as hospital type and level remain significant determinants influencing nursing leaders’ AI readiness. Implications for Nursing Management This study provides a standard measurement tool for Chinese hospitals to check how ready their nursing managers are for AI. These findings help us understand the current state of AI readiness among nursing managers in China. The results can help improve AI use strategies. In addition, they can help bring AI into nursing practice more smoothly. This study has important value for moving forward with digital change and smart nursing in China.
Hui Yang, Yuanzhi Guo, Yaxin Qiao et al.· Journal of Nursing Managemen...· 0 citations
Aim: This study was conducted to examine the psychometric properties of the Turkish version of the Nursing Practice Readiness Scale (NPRS).Design: The present research was designed as a methodological study. Data collection was carried out between February and May 2023, from a total of 225 participants, including 85 newly graduated nurses and 140 fourth-year nursing students.Methods: During the cultural adaptation process of the scale, forward translation was performed by three independent translators, the Turkish version was developed based on expert opinions, language equivalence was evaluated through back-translation, and a pilot study was conducted. Data were collected using a Descriptive Information Form, the NPRS, and the Work Readiness Scale for Graduate Nurses (WRS-GN). The scale’s language, content, and construct validity were assessed, while reliability was evaluated using Cronbach’s alpha coefficient, item-total score correlations, and parallel-form reliability. Data were analyzed using descriptive statistics, Pearson correlation analysis, and confirmatory factor analysis.Results: The content validity index of the scale was found to be 0.90. Items 33 and 35 were excluded due to low factor loading (0.579) and high residual covariance (5.775), respectively. After item removal, confirmatory factor analysis indicated acceptable model fit. The overall Cronbach’s alpha coefficient was calculated as 0.96, and item-total correlation coefficients ranged from 0.56 to 0.77. A statistically significant positive relationship was observed between NPRS and WRS-GN scores (r=0.739;p<0.001).Conclusions: The findings indicate that the 33-item Turkish version of the NPRS is a valid and reliable instrument for assessing the readiness of newly graduated nurses for clinical practice.
The TAILS-NS provides evidence of validity and reliability for measuring AI literacy among Thai nursing students and may serve as a standardized tool for curriculum evaluation.
Wannaporn Jongchidklang, Supalak Phonphithak, P. Amornritvanich· Journal of Educational Evalu...· 0 citations
Objective: The current methodological research aimed to translate and adapt the Self-Assessment of Informatics Competency Scale for Health Professionals (SANICS) for use in Türkiye. The SANICS was initially created to evaluate health workers’ self-efficacy in terms of informatics. In addition, the research aimed to analyze the validity and reliability of the Turkish SANICS.
Methods: This research was performed with 113 nurse educators in 11 undergraduate nursing programs during the 2022-2023 academic year in Ankara. The SANICS is a 5-point Likert scale comprising 18 questions with 3 sub-dimensions. Expert opinions confirmed the scale's linguistic validity, and 8 experts confirmed its scope validity. Minor corrections were made when needed. Both exploratory factor analysis and confirmatory factor analysis were used to evaluate the construct validity.
Results: The Turkish scale protected 3 sub-dimensions and 73.28% of the variance. Confirmatory factor analysis suggested that model fit indexes show acceptable fit. Cronbach’s α value for all scales was 0.92, and ranged between 0.86 and 0.91 for sub-dimensions. Spearman–Brown coefficient was found to be 0.865, and the correlation between the 2 halves in the test-retest analysis was 0.844. The lowest average score was seen in the applied computer skills subscale (2.10 ± 0.94), and the highest average score was seen in the basic computer skills subscale (3.22 ± 0.76).
Conclusion: Current research shows that the Turkish adaptation of SÇ-BYÖDÖ is a valid and reliable tool. This scale suggests a practical method to assess informatics self-efficacy among health workers and may, in the future, significantly contribute to the development of digital competency in Türkiye’s health system.
Nurhan Bingöl, Fatoş Korkmaz· Mediterranean Nursing and Mi...· 0 citations
Initial evidence is provided that the NAIRS is a valid and reliable instrument for assessing nursing students' readiness for artificial intelligence across knowledge/awareness, willingness to use AI, self-efficacy, and ethical awareness domains and may be useful for educational needs assessment and curriculum planning in nursing education.
Sumeyye Akçoban, Gülay Koca, S. Berşe· BMC Nursing· 0 citations