The Effect of Artificial Intelligence-Assisted Applications on the Perception of Nursing Values and the Mediating Role of Resilience

The Effect of Artificial Intelligence-Assisted Applications on the Perception of Nursing Values and the Mediating Role of Resilience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Artificial Intelligence-Assisted Applications on the Perception of Nursing Values and the Mediating Role of Resilience Şengül Üzen Cura, Selma Atay, Meltem Çimen, Emircan Işık This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9039729/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Artificial intelligence (AI)-assisted applications are becoming widespread in nursing practice and are creating a significant transformation in care processes. While this transformation may influence nurses’ perceptions of professional values, psychological resources such as resilience may shape how nurses adapt to this technological change. However, the role of resilience in this change remains unclear. Aim This study examined the relationship between nurses’ attitudes toward artificial intelligence-assisted applications and their perceptions of professional values, and tested whether resilience mediates this relationship. Method This cross-sectional study employed a correlational design with regression-based mediation analysis and was conducted with 301 nurses working in a university hospital. Data were collected between June and September 2025 using the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing, the Nurses’ Professional Values Scale-Revised, and the Brief Resilience Scale. Data analysis was performed using the Jamovi 2.6.22 statistical software package. Results A positive and significant relationship was found between AI attitude and professional value perception (β = 0.693, p < 0.01). A positive AI attitude was determined to have a direct and significant effect on the perception of professional values (β = 0.835, p < 0.01). The indirect effect of resilience was not statistically significant (β = − 0.005, p = 0.741), indicating that resilience did not mediate the relationship between AI attitude and professional values. Conclusion Nurses’ positive attitudes toward artificial intelligence were positively associated with their perception of professional values. Resilience does not assume a mediating role in this relationship. Artificial intelligence nursing professional values resilience digital transformation BACKGROUND The integration of artificial intelligence (AI)-assisted applications into healthcare services has initiated a comprehensive digital transformation process in nursing practices[ 1 , 2 ] Clinical decision support systems, patient monitoring algorithms, and administrative automation applications reduce the workload of nurses, making care processes more systematic and data-driven [ 3 ]. Research indicates that AI applications improve time management, enhance patient safety, and enable nurses to devote more time to direct care activities [ 2 , 4 ] Despite these operational advantages, the integration of AI into nursing practice raises important questions about how technological transformation may influence nurses’ professional identity and value systems. However, the limitations of AI technologies are also being discussed. The inability of algorithms to fully reflect contextual clinical judgment, holistically assess individualized care requirements, and replace human reasoning in ethical decision-making processes are significant areas of criticism [ 5 , 6 ]. In particular, data privacy, patient confidentiality, and the risks of algorithmic bias in decision-making processes lead to serious questioning in terms of nursing ethics [ 6 , 7 ]. The American Nurses Association (ANA) emphasizes that artificial intelligence should be used within an ethical framework and under nursing supervision; the technology should support nursing values but should not replace the human-centered approach to care [ 8 , 9 ]. At this point, the problem is not solely technological integration, but how nurses integrate this transformation with their professional values. The perception of professional values refers to the level of internalization of fundamental principles such as caregiving, justice, professionalism, and respect for human dignity [ 10 ]. In the literature, it is reported that nurses’ value-based decision-making approaches are significantly affected during technological change processes [ 11 , 12 ]. Attitudes toward AI-assisted applications may influence how nurses interpret, prioritize, and enact these professional values within technology-mediated care environments [ 2 , 13 ]. On the other hand, digital transformation brings about role ambiguity, new learning requirements, and restructuring processes in professional identity [ 14 ]. In this context, resilience is considered a critical individual resource for nurses to maintain their professional functions in an environment of change and uncertainty [ 15 , 16 ]. It is reported that nurses with high resilience adapt more quickly to change, have lower levels of burnout, and can maintain care quality more stably [ 17 , 18 ]. Furthermore, resilience is associated with the nurse’s capacity to preserve professional values under challenging conditions. According to the Conservation of Resources Theory, individuals can maintain their functionality in stressful situations thanks to the psychological resources they possess [ 19 ]. Within this framework, resilience can both facilitate adaptation to technological change and contribute to the maintenance of professional values. Therefore, resilience may function as a psychological resource that enables nurses to maintain their professional values while adapting to technological change, suggesting a potential mediating role in the relationship between AI attitudes and professional value perception. Although previous studies have examined the implementation of artificial intelligence in healthcare settings and separately explored professional values and resilience among nurses, empirical research investigating the direct relationship between nurses’ attitudes toward AI-assisted applications and their perception of professional values remains limited. Furthermore, no study to date has tested whether resilience mediates this relationship within the context of digital transformation in nursing practice. This gap in the literature limits understanding of the psychological mechanisms through which technological change may influence professional value systems. In this context, examining the relationship between nurses’ attitudes toward artificial intelligence-assisted applications and their perception of professional values, as well as the potential mediating role of resilience, is important for understanding not only the technical but also the ethical and professional dimensions of digital transformation in nursing. To achieve these purposes, the current research seeks to answer the following fundamental questions: Is there a significant relationship between nurses’ attitudes toward artificial intelligence-assisted applications and their perception of professional values? Does resilience mediate the relationship between nurses’ attitudes toward artificial intelligence-assisted applications and their perception of professional values? METHODS Study Design This cross-sectional study was conducted using a correlational design with regression-based mediation analysis to examine the relationships among artificial intelligence attitude, resilience, and professional values. Population and Sample of the Study The population of the study consisted of 448 nurses working in a university hospital. The sample size was calculated using G*Power 3.1.9.7. The minimum required sample size for multiple regression analysis was calculated as 268 using G*Power 3.1.9.7, based on an alpha level of 0.05, a statistical power of 0.99, an assumed small effect size (f² = 0.05), and three predictors included in the model. The study was completed with 301 participants. Nurses holding an associate, bachelor’s, or graduate degree who agreed to participate were included in the study. Inclusion criteria were as follows: [ 20 ] voluntarily agreeing to participate in the study, [ 20 ] holding an associate, bachelor’s, or graduate degree in nursing, (c) actively working as a nurse in the university hospital at the time of data collection, and [ 20 ] completing all data collection instruments in full. Study Variables: Independent Variable: Nurses’ attitudes toward artificial intelligence-assisted applications Mediating Variable: Resilience Dependent Variable: Nurses’ perception of professional values In the mediation model, attitudes toward artificial intelligence-assisted applications were specified as the predictor variable, professional values as the outcome variable, and resilience as the mediator. Data Collection Instruments The "Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing," the "Nurses Professional Values Scale-Revised," and the "Brief Resilience Scale" were used. Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN) The scale was developed by Yılmaz et al. (2025). The scale consists of a total of 15 items. It consists of two dimensions: positive attitude and negative attitude toward artificial intelligence technologies in nursing practices. Factor 1, consisting of the first six items, indicates negative attitudes toward the use of artificial intelligence technology in nursing, and Factor 2, consisting of items 7–15, indicates positive attitudes toward the use of artificial intelligence technology in nursing. Each item is on a 5-point Likert-type scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). The highest possible score from the scale is 75, and the lowest score is 15. Higher scores indicate a higher attitude. In the original study of the scale, the total Cronbach’s alpha value was calculated as 0.910, 0.933 for Factor 1, and 0.917 for Factor 2 [ 21 ]. In this study, the total Cronbach’s alpha value is 0.976, 0.927 for Factor 1, and 0.956 for Factor 2. The very high internal consistency coefficient may indicate substantial homogeneity among items. The Nurses Professional Values Scale-Revised (NPVS-R) The scale was developed by Weis and Schank (2009), and its Turkish validity and reliability study was conducted by Geçkil et al. (2012). The scale consists of a total of 26 items and five sub-dimensions: caregiving (Factor 1), professionalism (Factor 2), activism (Factor 3), justice (Factor 4), and trust (Factor 5). Each item is scored on a 5-point Likert-type scale ranging from "Not important" (1) to "Very important" (5). The scores that can be obtained from the scale range from 26 to 130, with higher scores indicating higher professional values. The Cronbach’s alpha value was found to be 0.94 in the original study and 0.92 in the Turkish validity-reliability study [ 10 , 22 ]. In this study, the Cronbach’s alpha value is 0.976. Brief Resilience Scale (BRS): The scale was developed by Smith et al. in 2008 and adapted into Turkish by Doğan in 2015. The scale, consisting of 6 items, is a 5-point Likert-type scale answered between 1 (Not at all applicable) and 5 (Completely applicable). Items 2, 4, and 6 are reverse-scored items; the total score is calculated after reversing the scores of these items. High scores indicate high resilience, while low scores indicate low resilience. In the Turkish adaptation study, the Cronbach’s alpha value was found to be 0.830 [ 23 ]. In this study, the Cronbach’s alpha value was calculated as 0.868. Administration of Data Collection Instruments The data of the study were collected between June and September 2025, after obtaining ethics committee approval and institutional permissions. Prior to data collection, participants were informed about the purpose, scope, and voluntariness principles of the study, and their written informed consent was obtained. The data were collected by the researchers through a questionnaire form using the face-to-face interview method. To minimize potential response bias, participants were informed that their responses would remain anonymous and would not affect their professional status. The interview with each participant took an average of 10 minutes. Throughout the data collection process, attention was paid to the principles of confidentiality and anonymity, and the identifying information of the participants was not recorded. Ethics approval and consent to participate Ethical approval for this study was granted by the Health Sciences Ethics Committee of Çanakkale Onsekiz Mart University, Türkiye with the decision dated 11.06.2025 and numbered 10/13 to conduct the study. Institutional permission was obtained from a hospital located in the province where the study would be conducted, with the decision dated 17.06.2025 and numbered E-27222899-622.99-2500152866. Written informed consent was obtained from all nurses who voluntarily participated in the study. Permission was obtained via e-mail from the author who developed the scale to use the scale. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Data Analysis Data analysis was performed using Jamovi 2.6.22 and SPSS v.27 statistical software packages. The mediating role of resilience was tested using PROCESS Macro (Model 4) developed by Hayes within the SPSS environment. Descriptive statistics were presented as mean ± standard deviation for continuous variables and as number and percentage (%) for categorical variables. The conformity of the data to a normal distribution was evaluated using the Shapiro-Wilk test and skewness-kurtosis values. In inter-group comparisons, independent samples t-test and one-way analysis of variance (ANOVA; Bonferroni post-hoc) were used in cases where the distribution was normal, while Mann-Whitney U and Kruskal-Wallis tests were used for data not showing normal distribution. The relationships between variables were examined using Pearson or Spearman correlation coefficients. Regression-based mediation analysis was applied to test the mediating role of resilience in the effect of AI attitude on the perception of nursing professional values. The statistical significance of the indirect effect was evaluated with the bootstrap method, which does not rely on parametric assumptions. In this context, 5,000 bootstrap resamplings were performed, and 95% confidence intervals were calculated. Direct effect (path c’), indirect effect (path a × b), and total effect (path c) coefficients were reported in the model. The explanatory power of the model was evaluated using the coefficient of determination (R²), and unstandardized regression coefficients [20], standardized coefficients (β), standard errors (SE), and 95% confidence intervals were reported. The level of statistical significance was accepted as p < 0.05. Limitations of the Study Since the study was limited to nurses working in only one university hospital, the findings obtained cannot be generalized to nurses working in different types of institutions (private hospitals, state hospitals, primary healthcare institutions, etc.). The data were collected using self-report questionnaire forms. This situation may create bias in the responses, considering that participants may tend to give socially acceptable answers. RESULTS The sample consisted of 301 nurses. More than half were aged 20–29 years (50.5%), 74.1% were female, and 35.9% worked in inpatient wards. Detailed demographic characteristics of the participants are presented in Table 1. The NPVS-R total score was 97.8 ± 19.63, and among the sub-dimensions, the highest mean score was observed in the Caring sub-dimension (32.0 ± 6.39), while the lowest mean score was in the Justice sub-dimension (11.4 ± 2.5). The ASUAITIN total score was 48.5 ± 9.2, and the sub-dimension scores were calculated as 15.6 ± 5.73 for negative attitude and 32.9 ± 8.23 for positive attitude. The BRS total mean score was 20.1 ± 4.79 (see Table 2). Positive and significant relationships were found between the NPVS-R total score and its sub-dimensions, and the ASUAITIN total score and the positive artificial intelligence attitude sub-dimension (p 0.05). A positive and significant correlation was found between the BRS scale and the trust sub-dimension of the NPVS-R, while a negative and significant correlation was found with the negative artificial intelligence attitude sub-dimension (p < 0.05) (see Table 3). A direct positive and significant relationship was determined between the attitude toward artificial intelligence-assisted applications and the perception of nursing professional values (β = 0.693, p < 0.01). The indirect effect evaluated through resilience was not found to be significant (indirect estimate = –0.022; 95% CI –0.068 – 0.006). The indirect effect ratio is at a low level (% mediation = 3.08). These findings indicate that resilience does not assume a distinct mediating role in this relationship. In the sub-dimension analyses, a positive artificial intelligence attitude was found to be strongly and significantly associated with the perception of nursing values (β = 0.835, p < 0.01). However, the indirect effect calculated through resilience is not significant (β = –0.005; 95% CI: –0.044 – 0.023; % mediation = 0.62). In contrast, both direct and indirect effects of the negative artificial intelligence attitude were not found to be statistically significant (p > 0.05) (see Table 4). DISCUSSION This study, conducted to evaluate the multidimensional effects of digital transformation on the nursing profession, has revealed that attitudes toward artificial intelligence are significantly associated with nurses’ perceptions of professional values. Furthermore, the research aimed to expand an underexplored area in literature by examining whether psychological resilience assumes a mediating role in this relationship. The findings demonstrate that technological integration in nursing is not merely a technical process; it also interacts with ethical, professional, and individual psychological dimensions. Indeed, it has been reported that digital transformation restructures nursing roles, professional identity, and care processes [20, 24]. In this study, it was determined that nurses’ perceptions of professional values were high. This result indicates that nurses demonstrate a strong commitment to core professional values and is consistent with the findings reported in the literature [11, 12]. Çamlı (2024) also emphasizes that nurses are guided primarily by moral/spiritual values, followed by professional values, in their professional practices. It is reported that values such as respect for human dignity, compassion, and honesty are closely related to the quality of care and patient satisfaction. When the NPVS-R sub-dimensions were examined, the caring sub-dimension was found to have the highest score, while the justice sub-dimension had the lowest. Similarly, it is stated in the literature that the value of "caring" is at the core of nursing identity, whereas the "justice" dimension is perceived to be relatively lower [25]. The high value of caring is an expected outcome, as care represents the human connection and ethical responsibility that constitute the essence of nursing [26]. In contrast, the lower perception of the justice dimension may be associated with organizational factors such as heavy workload, staff shortages, time constraints, and limited authority in decision-making processes[27] [28]. This situation suggests that the value of justice is influenced by systemic conditions rather than individual intentions. It was determined that nurses’ attitudes toward AI were generally positive. The fact that positive attitude scores were higher than negative attitude scores indicates that a supportive approach toward AI applications is dominant. This finding is in parallel with the results of Kandemir and Azizoğlu (2024). The fact that the majority of the participants were in the young age group suggests that familiarity with technology may have supported this positive attitude. Recent studies also report that nurses’ attitudes toward AI are generally positive and that they view technology as a supportive tool in care processes [29, 30]. Furthermore, it is stated that there is a strong perception that AI can enhance efficiency and patient safety in healthcare services [2, 3]. The positive relationship between AI attitude and the perception of professional values suggests that nurses who embrace technology are able to integrate these applications more holistically with their professional values. The fact that AI-supported systems reduce routine workload, thereby providing nurses with the opportunity to allocate more time to direct patient care [4, 31, 32], may contribute to the strengthening of the caring value. Moreover, it is reported that trust in technology increases among nurses who perceive a high contribution of AI to performance, and this supports professional practices [7, 33]. It is noteworthy that no significant relationship was found between a negative AI attitude and the perception of professional values. This result indicates that even if nurses have reservations about AI, they continue to uphold their professional values. It is stated that professional values are largely shaped by education, professional socialization, and ethical formation processes; therefore, they may be affected only to a limited extent by short-term attitudinal differences [34, 35]. In the regression analysis, it was determined that a positive AI attitude directly and strongly affects the perception of professional values. In contrast, it was observed that psychological resilience does not assume a significant mediating role in this relationship. In the literature, psychological resilience is reported to be effective on adaptation to change, professional identity development, and occupational commitment [18, 36]. In particular, it has been shown that psychological resilience can strengthen professional identity and support the individual’s professional orientation [36]. However, the current findings demonstrate that a positive attitude toward AI directly affects the perception of professional values; the level of psychological resilience is not a determining factor for this effect to emerge. This result suggests that professional values are deeper and more normative structures, shaped by professional identity and the ethical framework rather than individual psychological resources. Additionally, the strong effect of a positive attitude toward AI may have statistically limited the mediating role of psychological resilience. In this respect, the study reveals that the determining factor in preserving nursing values during the digital transformation process may be the way technology is conceptualized within the professional context, rather than individual resilience. CONCLUSION This study demonstrated that nurses’ attitudes toward artificial intelligence-supported applications are significantly and directly associated with their perceptions of professional values. In particular, a positive attitude toward artificial intelligence strongly predicts the perception of nursing values. However, it was determined that psychological resilience does not assume a significant mediating role in this relationship. The findings suggest that the preservation of nursing values during the digital transformation process may be related to the level of conceptualizing and adopting technology within a professional framework, rather than individual resilience. Supporting artificial intelligence applications with appropriate education and ethical guidance may contribute to an integration process that is congruent with nursing values. In future research, it is recommended to retest the model in different samples and to examine the role of organizational variables. Declarations Ethics approval and consent to participate Ethical approval for this study was granted by the Health Sciences Ethics Committee of Çanakkale Onsekiz Mart University, Türkiye with the decision dated 11.06.2025 and numbered 10/13 to conduct the study. Institutional permission was obtained from the hospital where the study was conducted, with the decision dated 17.06.2025 and numbered E-27222899-622.99-2500152866. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Prior to data collection, participants were informed about the purpose, scope, and voluntariness principles of the study, and their written informed consent was obtained. Consent for publication Not applicable Availability of data and materials The datasets are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors declare that no financial support was received for the research, authorship, and/or publication of this article. Authors' contributions ŞÜC designed the study. MC, SA, and EI contributed to drafting the manuscript. All authors participated in data collection. EI and MC managed the ethical procedures of the study. ŞÜC and SA critically revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. Acknowledgements The authors acknowledge the use of ChatGPT (OpenAI) and Grammarly for language editing and readability enhancement during the preparation of this manuscript. These tools were used solely to improve clarity and linguistic quality. All conceptualization, data collection, analysis, interpretation, and scientific content of the study were conducted by the researchers, and full responsibility for the content of the manuscript rests with the authors. References Cho KA, Seo YH. 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Personality portraits, resilience, and professional identity among nursing students: a cross-sectional study. Bmc Nurs. 2024;23(1):420. Tables Table 1 Descriptive Statistics of the Participants (n = 301) Variables n % Age 20–29 152 50.5 30–39 100 33.2 40 and above 49 16.3 Gender Female 223 74.1 Male 78 25.9 Marital Status Married 152 50.5 Single 149 49.5 Educational Status High School 32 10.6 Undergraduate 213 70.8 Postgraduate 56 18.6 Working Department Outpatient Clinic 24 8.0 Inpatient Ward 108 35.9 Intensive Care Unit 91 30.2 Operating Room 31 10.3 Emergency Department 47 15.6 Professional Experience 5 years and below 122 40.5 6–10 years 89 29.6 11 years and above 90 29.9 Experience in the Current Unit 1 year and below 75 24.9 2–5 years 142 47.2 6–9 years 63 20.9 10 years and above 21 7.0 Working Schedule Shift work 214 71.1 Daytime 87 28.9 Note. n: Number of participants. Table 2 Descriptive Statistics of the NPVS and AI (n = 301) NPVS Subscales Mean ± SD (Minimum-Maximum) Median (Percentiles 25th-75th) Caring 32.0 ± 6.39 (14–40) 33 (27–38) Professionalism 25.1 ± 5.62 (12–35) 25 (21–30) Activism 18.3 ± 4.37 (10–25) 18 (15–22) Justice 11.4 ± 2.50 (6–15) 12 (9–13) Trust 11.1 ± 2.60 (4–15) 11 (9–13) Total NPVS 97.8 ± 19.63 (48–130) 98 (81–114) AI Subscales AI Negative 15.6 ± 5.73 (6–30) 14 (12–19) AI Positive 32.9 ± 8.23 (9–45) 34 (28–38) Total AI 48.5 ± 9.20 (15–75) 49 (46–53) PR 20.1 ± 4.79 (6–30) 20 (17–24) Note. NPVS: Nurses' Professional Values Scale-Revised; SD: Standard Deviation; AI: Artificial Intelligence; PR: Psychological Resilience. Table 3 Correlation Between NPVS and Attitudes Toward AI (n = 301) Caring AI Negative AI Positive Total AI PR r 0.011 0.391* 0.357* 0.104 p 0.853 < .001 < .001 0.073 Professionalism r -0.027 0.282* 0.235* 0.043 p 0.636 < .001 < .001 0.455 Activism r -0.029 0.262* 0.217* 0.008 p 0.622 < .001 < .001 0.896 Justice r -0.019 0.332* 0.286* 0.026 p 0.748 < .001 < .001 0.650 Trust r -0.057 0.324* 0.254* 0.115* p 0.325 < .001 < .001 0.046 Total NPVS r -0.021 0.352* 0.302* 0.066 p 0.722 < .001 < .001 0.251 AI Negative r — -0.169* 0.472* -0.246* p — 0.003 < .001 < .001 AI Positive r -0.169* — 0.789* 0.066 p 0.003 — < .001 0.255 Total AI r 0.472* 0.789* — -0.094 p < .001 < .001 — 0.103 Note. NPVS: Nurses' Professional Values Scale-Revised; AI: Artificial Intelligence; PR: Psychological Resilience; r: Pearson correlation coefficient. *p < .05. Table 4 Regression Analysis: AI Attitudes, Resilience, and Nursing Value Perception (n = 301) 95% Confidence Interval Variable Effect Label Estimate 95% CI Lower 95% CI Upper p % Mediation Total AI Indirect Total AI → PR a × b -0.022 -0.068 0.006 0.250 3.08 Direct PR → F c 0.693 0.494 0.888 < .001 96.92 Total Total AI → F c + a × b 0.671 0.471 0.867 < .001 100.00 R = 0.345 R² = 0.119 p < .001 Negative AI Indirect Negative AI → PR a × b -0.035 -0.124 0.029 0.362 17.50 Direct PR → F c 0.163 -0.279 0.590 0.462 82.50 Total Negative AI → F c + a × b 0.128 -0.310 0.553 0.561 100.00 R = 0.079 R² = 0.006 p = 0.463 Positive AI Indirect Positive AI → PR a × b -0.005 -0.044 0.023 0.741 0.62 Direct PR → F c 0.835 0.598 1.059 < .001 99.38 Total Positive AI → F c + a × b 0.829 0.589 1.053 < .001 100.00 R = 0.363 R² = 0.132 p < .001 Note. AI: Artificial Intelligence; NPVS: Nurses' Professional Values Scale-Revised; PR: Psychological Resilience; CI: Confidence Interval; R: Multiple correlation coefficient; R²: Coefficient of determination. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Editor invited by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9039729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606428687,"identity":"c85d560b-fa81-4f84-aced-8e95c00f7c31","order_by":0,"name":"Şengül Üzen Cura","email":"","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":false,"prefix":"","firstName":"Şengül","middleName":"Üzen","lastName":"Cura","suffix":""},{"id":606428688,"identity":"fe286284-1208-4e4d-909c-68df2ca63e65","order_by":1,"name":"Selma Atay","email":"","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":false,"prefix":"","firstName":"Selma","middleName":"","lastName":"Atay","suffix":""},{"id":606428690,"identity":"c3907470-9ca8-41b2-bcb7-85e5af3e73aa","order_by":2,"name":"Meltem Çimen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYFAC5gYwxQYiPhgwyAApAwJaGBsbYFoYZxgw8BCvBWwhUD1hLbrtje0PPuYwyPOJHT722KbAjodfInkDw4+KbTi1mJ052Ng4cxuDYZt0WrpxjkEyj+SMtALGnjO3cWu5kdjYzLuNIYFNOsdMOsfgAI/BjRwDZsY2PFruP2xs/gvWkv9N2oIoLTcYG5sZIbawSTMQpeVMYuPM3m0SIL+YSfaA/NLzrOAgXr8cP3zgw89tNvLys5OfSfz4YyfHz5688cGPCtxaoEAClXuAkPpRMApGwSgYBfgBALr2UTIvIXcyAAAAAElFTkSuQmCC","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":true,"prefix":"","firstName":"Meltem","middleName":"","lastName":"Çimen","suffix":""},{"id":606428692,"identity":"8b9df807-bf74-4270-a10d-a2d425507b55","order_by":3,"name":"Emircan Işık","email":"","orcid":"","institution":"Çanakkale Onsekiz Mart University","correspondingAuthor":false,"prefix":"","firstName":"Emircan","middleName":"","lastName":"Işık","suffix":""}],"badges":[],"createdAt":"2026-03-05 11:55:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9039729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9039729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104782347,"identity":"37ea39e1-ab13-4ff2-95f8-dc485380a410","added_by":"auto","created_at":"2026-03-17 07:57:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":849333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9039729/v1/53c4307f-35fb-4be8-9fb8-b4cf3d42337e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Artificial Intelligence-Assisted Applications on the Perception of Nursing Values and the Mediating Role of Resilience","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe integration of artificial intelligence (AI)-assisted applications into healthcare services has initiated a comprehensive digital transformation process in nursing practices[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Clinical decision support systems, patient monitoring algorithms, and administrative automation applications reduce the workload of nurses, making care processes more systematic and data-driven [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Research indicates that AI applications improve time management, enhance patient safety, and enable nurses to devote more time to direct care activities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Despite these operational advantages, the integration of AI into nursing practice raises important questions about how technological transformation may influence nurses\u0026rsquo; professional identity and value systems.\u003c/p\u003e \u003cp\u003eHowever, the limitations of AI technologies are also being discussed. The inability of algorithms to fully reflect contextual clinical judgment, holistically assess individualized care requirements, and replace human reasoning in ethical decision-making processes are significant areas of criticism [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In particular, data privacy, patient confidentiality, and the risks of algorithmic bias in decision-making processes lead to serious questioning in terms of nursing ethics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The American Nurses Association (ANA) emphasizes that artificial intelligence should be used within an ethical framework and under nursing supervision; the technology should support nursing values but should not replace the human-centered approach to care [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt this point, the problem is not solely technological integration, but how nurses integrate this transformation with their professional values. The perception of professional values refers to the level of internalization of fundamental principles such as caregiving, justice, professionalism, and respect for human dignity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the literature, it is reported that nurses\u0026rsquo; value-based decision-making approaches are significantly affected during technological change processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Attitudes toward AI-assisted applications may influence how nurses interpret, prioritize, and enact these professional values within technology-mediated care environments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, digital transformation brings about role ambiguity, new learning requirements, and restructuring processes in professional identity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this context, resilience is considered a critical individual resource for nurses to maintain their professional functions in an environment of change and uncertainty [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is reported that nurses with high resilience adapt more quickly to change, have lower levels of burnout, and can maintain care quality more stably [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, resilience is associated with the nurse\u0026rsquo;s capacity to preserve professional values under challenging conditions. According to the Conservation of Resources Theory, individuals can maintain their functionality in stressful situations thanks to the psychological resources they possess [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Within this framework, resilience can both facilitate adaptation to technological change and contribute to the maintenance of professional values. Therefore, resilience may function as a psychological resource that enables nurses to maintain their professional values while adapting to technological change, suggesting a potential mediating role in the relationship between AI attitudes and professional value perception.\u003c/p\u003e \u003cp\u003eAlthough previous studies have examined the implementation of artificial intelligence in healthcare settings and separately explored professional values and resilience among nurses, empirical research investigating the direct relationship between nurses\u0026rsquo; attitudes toward AI-assisted applications and their perception of professional values remains limited. Furthermore, no study to date has tested whether resilience mediates this relationship within the context of digital transformation in nursing practice. This gap in the literature limits understanding of the psychological mechanisms through which technological change may influence professional value systems. In this context, examining the relationship between nurses\u0026rsquo; attitudes toward artificial intelligence-assisted applications and their perception of professional values, as well as the potential mediating role of resilience, is important for understanding not only the technical but also the ethical and professional dimensions of digital transformation in nursing. To achieve these purposes, the current research seeks to answer the following fundamental questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIs there a significant relationship between nurses\u0026rsquo; attitudes toward artificial intelligence-assisted applications and their perception of professional values?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes resilience mediate the relationship between nurses\u0026rsquo; attitudes toward artificial intelligence-assisted applications and their perception of professional values?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted using a correlational design with regression-based mediation analysis to examine the relationships among artificial intelligence attitude, resilience, and professional values.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation and Sample of the Study\u003c/h3\u003e\n\u003cp\u003eThe population of the study consisted of 448 nurses working in a university hospital. The sample size was calculated using G*Power 3.1.9.7. The minimum required sample size for multiple regression analysis was calculated as 268 using G*Power 3.1.9.7, based on an alpha level of 0.05, a statistical power of 0.99, an assumed small effect size (f\u0026sup2; = 0.05), and three predictors included in the model. The study was completed with 301 participants. Nurses holding an associate, bachelor\u0026rsquo;s, or graduate degree who agreed to participate were included in the study.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] voluntarily agreeing to participate in the study, [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] holding an associate, bachelor\u0026rsquo;s, or graduate degree in nursing, (c) actively working as a nurse in the university hospital at the time of data collection, and [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] completing all data collection instruments in full.\u003c/p\u003e\n\u003ch3\u003eStudy Variables:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIndependent Variable: Nurses\u0026rsquo; attitudes toward artificial intelligence-assisted applications\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMediating Variable: Resilience\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDependent Variable: Nurses\u0026rsquo; perception of professional values\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn the mediation model, attitudes toward artificial intelligence-assisted applications were specified as the predictor variable, professional values as the outcome variable, and resilience as the mediator.\u003c/p\u003e\n\u003ch3\u003eData Collection Instruments\u003c/h3\u003e\n\u003cp\u003eThe \"Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing,\" the \"Nurses Professional Values Scale-Revised,\" and the \"Brief Resilience Scale\" were used.\u003c/p\u003e\n\u003ch3\u003eAttitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN)\u003c/h3\u003e\n\u003cp\u003eThe scale was developed by Yılmaz et al. (2025). The scale consists of a total of 15 items. It consists of two dimensions: positive attitude and negative attitude toward artificial intelligence technologies in nursing practices. Factor 1, consisting of the first six items, indicates negative attitudes toward the use of artificial intelligence technology in nursing, and Factor 2, consisting of items 7\u0026ndash;15, indicates positive attitudes toward the use of artificial intelligence technology in nursing. Each item is on a 5-point Likert-type scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). The highest possible score from the scale is 75, and the lowest score is 15. Higher scores indicate a higher attitude. In the original study of the scale, the total Cronbach\u0026rsquo;s alpha value was calculated as 0.910, 0.933 for Factor 1, and 0.917 for Factor 2 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, the total Cronbach\u0026rsquo;s alpha value is 0.976, 0.927 for Factor 1, and 0.956 for Factor 2. The very high internal consistency coefficient may indicate substantial homogeneity among items.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe Nurses Professional Values Scale-Revised (NPVS-R)\u003c/h2\u003e \u003cp\u003eThe scale was developed by Weis and Schank (2009), and its Turkish validity and reliability study was conducted by Ge\u0026ccedil;kil et al. (2012). The scale consists of a total of 26 items and five sub-dimensions: caregiving (Factor 1), professionalism (Factor 2), activism (Factor 3), justice (Factor 4), and trust (Factor 5). Each item is scored on a 5-point Likert-type scale ranging from \"Not important\" (1) to \"Very important\" (5). The scores that can be obtained from the scale range from 26 to 130, with higher scores indicating higher professional values. The Cronbach\u0026rsquo;s alpha value was found to be 0.94 in the original study and 0.92 in the Turkish validity-reliability study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, the Cronbach\u0026rsquo;s alpha value is 0.976.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBrief Resilience Scale (BRS):\u003c/h3\u003e\n\u003cp\u003eThe scale was developed by Smith et al. in 2008 and adapted into Turkish by Doğan in 2015. The scale, consisting of 6 items, is a 5-point Likert-type scale answered between 1 (Not at all applicable) and 5 (Completely applicable). Items 2, 4, and 6 are reverse-scored items; the total score is calculated after reversing the scores of these items. High scores indicate high resilience, while low scores indicate low resilience. In the Turkish adaptation study, the Cronbach\u0026rsquo;s alpha value was found to be 0.830 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, the Cronbach\u0026rsquo;s alpha value was calculated as 0.868.\u003c/p\u003e\n\u003ch3\u003eAdministration of Data Collection Instruments\u003c/h3\u003e\n\u003cp\u003e The data of the study were collected between June and September 2025, after obtaining ethics committee approval and institutional permissions. Prior to data collection, participants were informed about the purpose, scope, and voluntariness principles of the study, and their written informed consent was obtained. The data were collected by the researchers through a questionnaire form using the face-to-face interview method. To minimize potential response bias, participants were informed that their responses would remain anonymous and would not affect their professional status. The interview with each participant took an average of 10 minutes. Throughout the data collection process, attention was paid to the principles of confidentiality and anonymity, and the identifying information of the participants was not recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the\u0026nbsp;Health Sciences Ethics Committee of \u0026Ccedil;anakkale Onsekiz Mart University, T\u0026uuml;rkiye with the decision dated 11.06.2025 and numbered 10/13 to conduct the study. Institutional permission was obtained from a hospital located in the province where the study would be conducted, with the decision dated 17.06.2025 and numbered E-27222899-622.99-2500152866. Written informed consent was obtained from all nurses who voluntarily participated in the study.\u0026nbsp;Permission was obtained via e-mail from the author who developed the scale to use the scale. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed using Jamovi 2.6.22 and SPSS v.27 statistical software packages. The mediating role of resilience was tested using PROCESS Macro (Model 4) developed by Hayes within the SPSS environment. Descriptive statistics were presented as mean \u0026plusmn; standard deviation for continuous variables and as number and percentage (%) for categorical variables. The conformity of the data to a normal distribution was evaluated using the Shapiro-Wilk test and skewness-kurtosis values.\u003c/p\u003e\n\u003cp\u003eIn inter-group comparisons, independent samples t-test and one-way analysis of variance (ANOVA; Bonferroni post-hoc) were used in cases where the distribution was normal, while Mann-Whitney U and Kruskal-Wallis tests were used for data not showing normal distribution. The relationships between variables were examined using Pearson or Spearman correlation coefficients.\u003c/p\u003e\n\u003cp\u003eRegression-based mediation analysis was applied to test the mediating role of resilience in the effect of AI attitude on the perception of nursing professional values. The statistical significance of the indirect effect was evaluated with the bootstrap method, which does not rely on parametric assumptions. In this context, 5,000 bootstrap resamplings were performed, and 95% confidence intervals were calculated. Direct effect (path c\u0026rsquo;), indirect effect (path a \u0026times; b), and total effect (path c) coefficients were reported in the model. The explanatory power of the model was evaluated using the coefficient of determination (R\u0026sup2;), and unstandardized regression coefficients [20], standardized coefficients (\u0026beta;), standard errors (SE), and 95% confidence intervals were reported. The level of statistical significance was accepted as p \u0026lt; 0.05.\u003c/p\u003e\n\u003ch2\u003eLimitations of the Study\u003c/h2\u003e\n\u003cp\u003eSince the study was limited to nurses working in only one university hospital, the findings obtained cannot be generalized to nurses working in different types of institutions (private hospitals, state hospitals, primary healthcare institutions, etc.). The data were collected using self-report questionnaire forms. This situation may create bias in the responses, considering that participants may tend to give socially acceptable answers.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe sample consisted of 301 nurses. More than half were aged 20\u0026ndash;29 years (50.5%), 74.1% were female, and 35.9% worked in inpatient wards. Detailed demographic characteristics of the participants are presented in Table 1. The NPVS-R total score was 97.8 \u0026plusmn; 19.63, and among the sub-dimensions, the highest mean score was observed in the Caring sub-dimension (32.0 \u0026plusmn; 6.39), while the lowest mean score was in the Justice sub-dimension (11.4 \u0026plusmn; 2.5). The ASUAITIN total score was 48.5 \u0026plusmn; 9.2, and the sub-dimension scores were calculated as 15.6 \u0026plusmn; 5.73 for negative attitude and 32.9 \u0026plusmn; 8.23 for positive attitude. The BRS total mean score was 20.1 \u0026plusmn; 4.79 (see Table 2).\u003c/p\u003e\n\u003cp\u003ePositive and significant relationships were found between the NPVS-R total score and its sub-dimensions, and the ASUAITIN total score and the positive artificial intelligence attitude sub-dimension (p \u0026lt; 0.05). Conversely, no statistically significant relationship was found between negative artificial intelligence attitude and NPVS-R scores (p \u0026gt; 0.05). A positive and significant correlation was found between the BRS scale and the trust sub-dimension of the NPVS-R, while a negative and significant correlation was found with the negative artificial intelligence attitude sub-dimension (p \u0026lt; 0.05) (see Table 3).\u003c/p\u003e\n\u003cp\u003eA direct positive and significant relationship was determined between the attitude toward artificial intelligence-assisted applications and the perception of nursing professional values (\u0026beta; = 0.693, p \u0026lt; 0.01). The indirect effect evaluated through resilience was not found to be significant (indirect estimate = \u0026ndash;0.022; 95% CI \u0026ndash;0.068 \u0026ndash; 0.006). The indirect effect ratio is at a low level (% mediation = 3.08). These findings indicate that resilience does not assume a distinct mediating role in this relationship. In the sub-dimension analyses, a positive artificial intelligence attitude was found to be strongly and significantly associated with the perception of nursing values (\u0026beta; = 0.835, p \u0026lt; 0.01). However, the indirect effect calculated through resilience is not significant (\u0026beta; = \u0026ndash;0.005; 95% CI: \u0026ndash;0.044 \u0026ndash; 0.023; % mediation = 0.62). In contrast, both direct and indirect effects of the negative artificial intelligence attitude were not found to be statistically significant (p \u0026gt; 0.05) (see Table 4).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study, conducted to evaluate the multidimensional effects of digital transformation on the nursing profession, has revealed that attitudes toward artificial intelligence are significantly associated with nurses\u0026rsquo; perceptions of professional values. Furthermore, the research aimed to expand an underexplored area in literature by examining whether psychological resilience assumes a mediating role in this relationship. The findings demonstrate that technological integration in nursing is not merely a technical process; it also interacts with ethical, professional, and individual psychological dimensions. Indeed, it has been reported that digital transformation restructures nursing roles, professional identity, and care processes [20, 24].\u003c/p\u003e\n\u003cp\u003eIn this study, it was determined that nurses\u0026rsquo; perceptions of professional values were high. This result indicates that nurses demonstrate a strong commitment to core professional values and is consistent with the findings reported in the literature [11, 12]. \u0026Ccedil;amlı (2024) also emphasizes that nurses are guided primarily by moral/spiritual values, followed by professional values, in their professional practices. It is reported that values such as respect for human dignity, compassion, and honesty are closely related to the quality of care and patient satisfaction.\u003c/p\u003e\n\u003cp\u003eWhen the NPVS-R sub-dimensions were examined, the caring sub-dimension was found to have the highest score, while the justice sub-dimension had the lowest. Similarly, it is stated in the literature that the value of \u0026quot;caring\u0026quot; is at the core of nursing identity, whereas the \u0026quot;justice\u0026quot; dimension is perceived to be relatively lower [25]. The high value of caring is an expected outcome, as care represents the human connection and ethical responsibility that constitute the essence of nursing [26]. In contrast, the lower perception of the justice dimension may be associated with organizational factors such as heavy workload, staff shortages, time constraints, and limited authority in decision-making processes[27] [28]. This situation suggests that the value of justice is influenced by systemic conditions rather than individual intentions.\u003c/p\u003e\n\u003cp\u003eIt was determined that nurses\u0026rsquo; attitudes toward AI were generally positive. The fact that positive attitude scores were higher than negative attitude scores indicates that a supportive approach toward AI applications is dominant. This finding is in parallel with the results of Kandemir and Azizoğlu (2024). The fact that the majority of the participants were in the young age group suggests that familiarity with technology may have supported this positive attitude. Recent studies also report that nurses\u0026rsquo; attitudes toward AI are generally positive and that they view technology as a supportive tool in care processes [29, 30]. Furthermore, it is stated that there is a strong perception that AI can enhance efficiency and patient safety in healthcare services [2, 3].\u003c/p\u003e\n\u003cp\u003eThe positive relationship between AI attitude and the perception of professional values suggests that nurses who embrace technology are able to integrate these applications more holistically with their professional values. The fact that AI-supported systems reduce routine workload, thereby providing nurses with the opportunity to allocate more time to direct patient care [4, 31, 32], may contribute to the strengthening of the caring value. Moreover, it is reported that trust in technology increases among nurses who perceive a high contribution of AI to performance, and this supports professional practices [7, 33].\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that no significant relationship was found between a negative AI attitude and the perception of professional values. This result indicates that even if nurses have reservations about AI, they continue to uphold their professional values. It is stated that professional values are largely shaped by education, professional socialization, and ethical formation processes; therefore, they may be affected only to a limited extent by short-term attitudinal differences [34, 35].\u003c/p\u003e\n\u003cp\u003eIn the regression analysis, it was determined that a positive AI attitude directly and strongly affects the perception of professional values. In contrast, it was observed that psychological resilience does not assume a significant mediating role in this relationship. In the literature, psychological resilience is reported to be effective on adaptation to change, professional identity development, and occupational commitment [18, 36]. In particular, it has been shown that psychological resilience can strengthen professional identity and support the individual\u0026rsquo;s professional orientation [36]. However, the current findings demonstrate that a positive attitude toward AI directly affects the perception of professional values; the level of psychological resilience is not a determining factor for this effect to emerge.\u003c/p\u003e\n\u003cp\u003eThis result suggests that professional values are deeper and more normative structures, shaped by professional identity and the ethical framework rather than individual psychological resources. Additionally, the strong effect of a positive attitude toward AI may have statistically limited the mediating role of psychological resilience. In this respect, the study reveals that the determining factor in preserving nursing values during the digital transformation process may be the way technology is conceptualized within the professional context, rather than individual resilience.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrated that nurses\u0026rsquo; attitudes toward artificial intelligence-supported applications are significantly and directly associated with their perceptions of professional values. In particular, a positive attitude toward artificial intelligence strongly predicts the perception of nursing values. However, it was determined that psychological resilience does not assume a significant mediating role in this relationship.\u003c/p\u003e\n\u003cp\u003eThe findings suggest that the preservation of nursing values during the digital transformation process may be related to the level of conceptualizing and adopting technology within a professional framework, rather than individual resilience. Supporting artificial intelligence applications with appropriate education and ethical guidance may contribute to an integration process that is congruent with nursing values.\u003c/p\u003e\n\u003cp\u003eIn future research, it is recommended to retest the model in different samples and to examine the role of organizational variables.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Health Sciences Ethics Committee of Çanakkale Onsekiz Mart University, Türkiye with the decision dated 11.06.2025 and numbered 10/13 to conduct the study. Institutional permission was obtained from the hospital where the study was conducted, with the decision dated 17.06.2025 and numbered E-27222899-622.99-2500152866. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Prior to data collection, participants were informed about the purpose, scope, and voluntariness principles of the study, and their written informed consent was obtained.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial support was received for the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eŞÜC designed the study. MC, SA, and EI contributed to drafting the manuscript. All authors participated in data collection. EI and MC managed the ethical procedures of the study. ŞÜC and SA critically revised the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of ChatGPT (OpenAI) and Grammarly for language editing and readability enhancement during the preparation of this manuscript. These tools were used solely to improve clarity and linguistic quality. All conceptualization, data collection, analysis, interpretation, and scientific content of the study were conducted by the researchers, and full responsibility for the content of the manuscript rests with the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCho KA, Seo YH. 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Bmc Nurs. 2024;23(1):420.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eDescriptive Statistics of the Participants (n\u0026thinsp;=\u0026thinsp;301)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariables\u003c/div\u003e \u003c/th\u003e 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class=\"SimplePara\"\u003e25.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eMarital Status\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMarried\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e152\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSingle\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e149\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e49.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eEducational Status\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHigh School\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eUndergraduate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e213\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e70.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePostgraduate\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e56\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWorking Department\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOutpatient Clinic\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e8.0\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eInpatient Ward\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e108\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e35.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIntensive Care Unit\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e91\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e30.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eOperating Room\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e10.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eEmergency Department\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e47\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eProfessional Experience\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e5 years and below\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e122\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e40.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;10 years\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e89\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e29.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 years and above\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e90\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e29.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eExperience in the Current Unit\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1 year and below\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e75\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e24.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u0026ndash;5 years\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e142\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e47.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u0026ndash;9 years\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e63\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e10 years and above\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7.0\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWorking Schedule\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eShift work\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e214\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e71.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDaytime\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e87\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e28.9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. n: Number of participants.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eDescriptive Statistics of the NPVS and AI (n\u0026thinsp;=\u0026thinsp;301)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNPVS Subscales\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Minimum-Maximum)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eMedian (Percentiles 25th-75th)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCaring\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e32.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39 (14\u0026ndash;40)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e33 (27\u0026ndash;38)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eProfessionalism\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62 (12\u0026ndash;35)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e25 (21\u0026ndash;30)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eActivism\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37 (10\u0026ndash;25)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e18 (15\u0026ndash;22)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eJustice\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50 (6\u0026ndash;15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e12 (9\u0026ndash;13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTrust\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60 (4\u0026ndash;15)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e11 (9\u0026ndash;13)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal NPVS\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e97.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.63 (48\u0026ndash;130)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e98 (81\u0026ndash;114)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAI Subscales\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAI Negative\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.73 (6\u0026ndash;30)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e14 (12\u0026ndash;19)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAI Positive\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23 (9\u0026ndash;45)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e34 (28\u0026ndash;38)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal AI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e48.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.20 (15\u0026ndash;75)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e49 (46\u0026ndash;53)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79 (6\u0026ndash;30)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e20 (17\u0026ndash;24)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNote.\u003c/span\u003e NPVS: Nurses' Professional Values Scale-Revised; SD: Standard Deviation; AI: Artificial Intelligence; PR: Psychological Resilience.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eCorrelation Between NPVS and Attitudes Toward AI (n\u0026thinsp;=\u0026thinsp;301)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eCaring\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eAI Negative\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eAI Positive\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal AI\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.011\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.391*\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.357*\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.104\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.853\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.073\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eProfessionalism\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.027\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.282*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.235*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.043\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.636\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.455\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eActivism\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.029\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.262*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.217*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.008\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.622\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.896\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eJustice\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.019\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.332*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.286*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.026\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.748\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.650\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTrust\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.057\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.324*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.254*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.115*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.325\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.046\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTotal NPVS\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.021\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.352*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.302*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.066\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.722\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.251\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAI Negative\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.169*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.472*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.246*\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.003\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAI Positive\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.169*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.789*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.066\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.003\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.255\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTotal AI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003er\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.472*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.789*\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.094\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026mdash;\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.103\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote. NPVS: Nurses' Professional Values Scale-Revised; AI: Artificial Intelligence; PR: Psychological Resilience; r: Pearson correlation coefficient. *p \u0026lt; .05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRegression Analysis: AI Attitudes, Resilience, and Nursing Value Perception (n\u0026thinsp;=\u0026thinsp;301)\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% Confidence Interval\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEffect\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eLabel\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eEstimate\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% CI Lower\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% CI Upper\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003ep\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e% Mediation\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal AI\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIndirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal AI \u0026rarr; PR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ea \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.022\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.068\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.006\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.250\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.08\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.693\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.494\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.888\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e96.92\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal AI \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u0026thinsp;+\u0026thinsp;a \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.671\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.471\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.867\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e100.00\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.345\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u0026sup2;\u003c/span\u003e = 0.119\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e \u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eNegative AI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIndirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNegative AI \u0026rarr; PR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ea \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.035\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.124\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.029\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.362\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e17.50\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.163\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.279\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.590\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.462\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e82.50\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNegative AI \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u0026thinsp;+\u0026thinsp;a \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.128\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.310\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.553\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.561\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e100.00\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.079\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u0026sup2;\u003c/span\u003e = 0.006\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.463\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003ePositive AI\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eIndirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePositive AI \u0026rarr; PR\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ea \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.005\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.044\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.023\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.741\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.62\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eDirect\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePR \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.835\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.598\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.059\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e99.38\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTotal\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003ePositive AI \u0026rarr; F\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ec\u0026thinsp;+\u0026thinsp;a \u0026times; b\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.829\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.589\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.053\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e100.00\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u003c/span\u003e\u0026thinsp;=\u0026thinsp;0.363\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eR\u0026sup2;\u003c/span\u003e = 0.132\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e \u0026lt;\u0026thinsp;.001\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eNote.\u003c/span\u003e AI: Artificial Intelligence; NPVS: Nurses' Professional Values Scale-Revised; PR: Psychological Resilience; CI: Confidence Interval; R: Multiple correlation coefficient; R\u0026sup2;: Coefficient of determination.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, nursing, professional values, resilience, digital transformation","lastPublishedDoi":"10.21203/rs.3.rs-9039729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9039729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence (AI)-assisted applications are becoming widespread in nursing practice and are creating a significant transformation in care processes. While this transformation may influence nurses’ perceptions of professional values, psychological resources such as resilience may shape how nurses adapt to this technological change. However, the role of resilience in this change remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined the relationship between nurses’ attitudes toward artificial intelligence-assisted applications and their perceptions of professional values, and tested whether resilience mediates this relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study employed a correlational design with regression-based mediation analysis and was conducted with 301 nurses working in a university hospital. Data were collected between June and September 2025 using the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing, the Nurses’ Professional Values Scale-Revised, and the Brief Resilience Scale. Data analysis was performed using the Jamovi 2.6.22 statistical software package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA positive and significant relationship was found between AI attitude and professional value perception (β = 0.693, p \u0026lt; 0.01). A positive AI attitude was determined to have a direct and significant effect on the perception of professional values (β = 0.835, p \u0026lt; 0.01). The indirect effect of resilience was not statistically significant (β = − 0.005, p = 0.741), indicating that resilience did not mediate the relationship between AI attitude and professional values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNurses’ positive attitudes toward artificial intelligence were positively associated with their perception of professional values. Resilience does not assume a mediating role in this relationship.\u003c/p\u003e","manuscriptTitle":"The Effect of Artificial Intelligence-Assisted Applications on the Perception of Nursing Values and the Mediating Role of Resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 12:17:29","doi":"10.21203/rs.3.rs-9039729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T07:22:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T06:17:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T11:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203074324586912153078281867212006739470","date":"2026-03-18T06:43:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203176232897673255425359328439372555523","date":"2026-03-12T16:50:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-12T15:43:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T15:41:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T14:53:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T08:15:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2026-03-11T12:37:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98577f80-aeab-4231-a17f-185ea132d389","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T11:41:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 12:17:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9039729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9039729","identity":"rs-9039729","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}