Nursing Informatics Competence as a Predictor of Nursing Performance: A Comparison of AI and Human Statistical Analyses

Nursing Informatics Competence as a Predictor of Nursing Performance: A Comparison of AI and Human Statistical Analyses | 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 Nursing Informatics Competence as a Predictor of Nursing Performance: A Comparison of AI and Human Statistical Analyses Seon Mi Jang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7167912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The digitalization of healthcare has highlighted the growing importance of nursing informatics competence, while the use of artificial intelligence in healthcare research continues to expand. This study aimed to examine the impact of nursing informatics competence on nursing performance and to compare the accuracy of statistical analyses conducted by AI and a human researcher. Methods: A cross-sectional study was conducted with 48 clinical nurses. Nursing informatics competence was measured using the Korean Nursing Informatics Competence Assessment Scale, and nursing performance was assessed with a validated appraisal tool. Data were collected by structured questionnaires and analyzed with SPSS and ChatGPT. Analyses included descriptive statistics, independent t-tests, Pearson’s correlation, and linear regression. Results from AI-assisted analyses were compared with those obtained through traditional statistical methods. Results: Nursing informatics competence showed a significant positive correlation with nursing performance, and regression analysis confirmed it as a significant predictor (β = .63, p < .001), explaining 38.5% of the variance. Among the nursing informatics competence subdomains, “Use of ICT in Nursing” showed the strongest association with nursing performance. Statistical analyses performed by AI and the human researcher were highly consistent. Conclusions: Nursing informatics competence is a significant predictor of nursing performance. AI-based statistical analysis showed strong agreement with traditional methods, suggesting its potential as a supplementary tool in nursing research. Nursing Informatics Clinical Competence Work Performance Artificial Intelligence 1. Background Healthcare institutions aim to enhance the efficiency and effectiveness of medical services through digitalization and to realize patient-centered care based on the active participation of both patients and healthcare professionals. In this digital healthcare environment, the work of nurses is becoming increasingly complex, and the importance of nursing informatics competence (NIC) is emphasized to improve nursing performance (NP). NIC refers to the ability to support the integration of information during nursing practice by utilizing Information and Communication Technology (ICT), while adhering to professional standards and regulations as nurses [ 1 ]. In the digital healthcare environment, it is natural for nurses to be required to possess NIC, as it is an essential capability for providing safe and high-quality nursing care [ 2 ]. Furthermore, NIC is closely related to nursing practice, and previous studies have identified it as a significant factor influencing NP [ 3 ]. NP refers to the outcomes of functional tasks that nurses carry out in clinical settings over a specified period [ 4 ], encompassing the quality and effectiveness of their professional duties. Recent studies have identified a statistically significant positive correlation between NIC and NP, indicating that nurses with higher informatics competence tend to demonstrate better performance in their roles [ 3 ]. Given this relationship, this study aimed to examine the influence of NIC on NP, positioning informatics competence as a key factor in enhancing nursing practice. In recent years, Artificial Intelligence (AI) has rapidly gained prominence and is increasingly being applied across diverse fields, including education, research, and clinical practice. AI improves the efficiency and accessibility of exploratory data analysis, making it particularly useful for researchers with statistical insight but limited programming experience [ 5 ]. Accordingly, this study seeks to examine the capability of AI in performing statistical analyses and explore its applicability in nursing research by comparing AI-assisted statistical results with traditional analysis methods. Accordingly, this study aimed to explore the potential of utilizing AI in nursing research by evaluating its statistical analysis capabilities and comparing the results with those obtained through traditional methods. The objectives of this study were to : ● Assess the level of NIC and identify related factors. ● Determine the impact of NIC on NP. ● Evaluate the performance of AI in statistical analysis by comparing its results with those of traditional methods, to assess accuracy and potential applicability in future nursing research. 2. Methods 1) Research Design This study was a descriptive research study conducted to examine the effect of NIC on NP among nurses. 2) Participants The participants of this study were general nurses working in clinical settings. The inclusion criteria for the study participants were as follows: - Nurses employed at general hospitals or other hospitals. - General nurses who perform direct patient care in wards, operating rooms, intensive care units, emergency rooms, or outpatient clinics. - Those who understood the purpose of this study and provided informed, voluntary consent to participate. The sample size was determined using the G*Power 3.1 program, based on linear regression analysis. The independent variable was set as NIC, and the dependent variable as NP. The effect size (f²) was set to 0.30, referencing prior studies [6,7] that reported effect sizes of 0.32 and 0.36 for the relationship between NIC and work performance. The significance level (α) was set at 0.05 and the statistical power (1–β) at 0.80. As a result, the minimum required sample size was calculated to be 29 participants. 3) Research Instruments and Measurement Variables (1) Nursing Informatics Competence Nursing informatics competence was measured using the Korean Nursing Informatics Competence Assessment Scale (K-NICAS) [8], developed by Jang and Kim. This instrument consists of a total of 20 items, divided into five subdomains: Basic ICT Use (3 items), Utilization and Management of Nursing Information (5 items), Professional Responsibility and Ethics (5 items), Use of ICT in Nursing Practice (4 items), and Attitude Toward Nursing Informatics (3 items). The responses are measured on a 4-point Likert scale (1 to 4), with higher total scores indicating higher levels of NIC. The reliability of the tool was .91 in Cronbach’s α at the time of its development and .88 in this study. (2) Nursing Performance In this study, nursing performance was measured using the nurse performance appraisal tool developed by Park et al. [4] This instrument consists of a total of 41 items, categorized into three subdomains: Nursing Care Provision Function (29 items), Nursing Support Function (6 items), and Communication and Interpersonal Relationship Function (6 items). Each item is rated on a 5-point Likert scale (0 = not at all, 4 = very well), with higher scores indicating a greater level of NP. The reliability of the tool was .96 in Cronbach’s α at the time of its development and .98 in this study. 4) Data Collection Data collection for this study was conducted in October 2022 using a survey method. The questionnaire consisted of a total of 68 questions to measure the general characteristics of the subjects and research variables. After explaining the purpose and procedure of the study to the nursing department manager at each institution and requesting cooperation, Recruitment notices for study subjects and survey questionnaires were distributed to each ward. 5) Data Analysis Data were analyzed using SPSS version 23 and ChatGPT. The following procedures were performed: (1) Descriptive statistics were calculated to summarize the levels of NIC and NP. (2) Differences in NIC and nursing NP based on the participants’ general characteristics were analyzed using independent t-tests. (3) Pearson’s correlation coefficients were used to analyze the relationship between NIC and NP. (4) Linear regression analysis was conducted to assess the effect of NIC on NP. (5) To evaluate the statistical analysis ability of AI, the same dataset was analyzed using ChatGPT, and the results were compared with those obtained through traditional analysis using SPSS. 6) Ethical Considerations This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of Dongshin University. Prior to completing the questionnaire, participants were provided with a written explanation outlining the purpose of the study, the voluntary nature of participation, assurance of anonymity, and their right to withdraw from the study at any time. Participants were given sufficient time to read and fully understand the written explanation before signing the informed consent form. Written informed consent was obtained from all participants before data collection. Only those who provided written consent participated in the survey. All collected data were anonymized and coded to ensure confidentiality and were used solely for research purposes, and no personal information was collected. Only phone numbers were collected exclusively from those who wished to receive compensation (a mobile coupon) for participating in the study. 3. Results 1) Nursing Informatics Competence and Nursing Performance by Participant Characteristics Among the 48 participants in this study, the majority were female (89.6%). The mean age was 30.87±7.28 years. The average duration of nursing experience was 76.08 months. Regarding the type of institution, 62.5% of the participants were employed at general hospitals, representing the highest proportion. Additionally, 16 nurses (33.3%) received nursing informatics education in undergraduate courses (Table 1). Table 1. NIC and NP by Participant Characteristics ( N =48) Characteristics Categories n(%) or M±SD NIC NP M±SD t/F/r ( ρ ) M±SD t/F ( ρ ) Gender Female 43 (89.6) 2.84±.45 1.25 (.216) 3.02±.64 .67 (.509) Male 5 (10.4) 2.58±.38 2.82±.34 Age 30.87±7.28 r=.04 (.807) r=.15 (.306) Nursing Experience (month) 76.08±89.26 r=.09 (.527) r=.21 (.153) Education Associate 12 (25.0) 2.86±.47 .38 (.707) 3.12±.62 .82 (.418) ≥ Bachelor’s degree 36 (23.8) 2.80±.45 2.95±.62 Type of Hospital Clinic 18 (37.5) 2.69±.35 -1.55 (.129) 2.86±.52 -1.16 (.253) General 30 (62.5) 2.89±.49 3.08±.67 Working Department Ward 22 (45.8) 2.80±.48 -.22 (.826) 2.86±.70 -1.44 (.158) non-Ward 26 (54.2) 2.83±.43 3.11±.53 Experience of NIE Yes 16 (33.3) 2.87±.51 .58 (.566) 2.86±.62 -1.08 (.284) No 32 (66.7) 2.79±.42 3.06±.62 NIE=nursing informatics education Before analyzing differences in NIC and NP by general characteristics of the participants, tests for normality and homogeneity of variance were conducted. Although the sample size per group did not meet the recommended threshold of 30 for independent t-tests (total participants: 48), the dependent variables were confirmed to follow a normal distribution and demonstrated homogeneity of variance. Therefore, the independent samples t-test was deemed appropriate and applied for group comparisons (Table 2). Table 2. Test of Normality and Homogeneity of Variance for the Dependent Variable NP by Group Variable Categories n Shapiro – Wilk (Normality) Levene ’ s Test (Homogeneity) W Statistic p-value F Statistic p-value Gender Female 43 .956 .101 3.043 .088 Male 5 .908 .455 Education Level Associate 12 .923 .312 .264 .610 ≥Bachelor’s degree 36 .973 .499 Type of Hospital Clinic 18 .935 .238 2.851 .098 General 30 .952 .196 Working Department Ward 22 .967 .643 1.364 .249 Non-Ward 26 .917 .037 Experience of NIE Yes 16 .982 .976 .272 .605 No 32 .958 .249 The analysis results showed that the differences in NIC by general characteristics were not statistically significant. Although the average score of NIC for nurses working in general hospitals was higher than that of nurses working in hospital-level institutions, there was no statistically significant difference. Similarly, nurses who had received nursing informatics education in undergraduate courses had higher competence scores compared to those without such education, but this difference also did not reach statistical significance. No significant differences in NP were observed based on the general characteristics of the participants. 2) Levels of Nursing Informatics Competence and Nursing Performance The mean score for NIC among the study participants was 2.82 (±0.45), with scores ranging from 2.05 to 3.85. Among the subdomains, ‘Attitude toward Nursing Informatics’ had the highest at 3.44, and ‘Nursing Information Utilization and Management’ had the lowest at 2.54. The mean score for NP was 2.95 (±0.65). For the subdomains, ‘Nursing Support Function’ showed the highest mean score at 3.15, and ‘Nursing Care Provision Function’ had the lowest at 2.95(Table 3). Table 3. Scores of NIC and NP ( N = 48) Categories subdomain M ± SD range Nursing Informatics Competence total 2.82±.45 2.05∼3.85 F1. Basic ICT use 2.78±.74 1.00∼4.00 F2. Nursing Information Utilization and Management 2.54±.54 1.60∼4.00 F3. Professional Responsibility and Ethics 2.88±.61 1.60∼4.00 F4. Use of ICT in Nursing 2.65±.67 1.50∼4.00 F5. Attitude toward Nursing Informatics 3.44±.59 2.00∼4.00 Nursing Performance total 2.99±.62 1.59∼4.00 F1. Nursing Care Provision Function 2.95±.65 1.48∼4.00 F2. Nursing Support Function 3.15±.67 1.50∼4.00 F3. Communication and Interpersonal Relationship Function 3.08±.68 1.83∼4.00 3) Correlations between Nursing Informatics Competence and Nursing Performance A statistically significant positive correlation was found between NIC and NP, with a correlation coefficient of r = .632 (p< .001). Among the subdomains of NIC, ‘Use of ICT in Nursing’ showed the strongest correlation with overall NP (r=.599, p<.001), and NIC showed the strongest correlation with the ‘Nursing Provision Function’ subdomain of NP (r=.612, p<.001). Specifically, the ‘Use of ICT in Nursing’ subdomain of NIC showed the strongest correlation with the ‘Nursing Provision Function’ subdomain of NP (r=.621, p<.001) (Table 4). Table 4. Correlation between NIC and NP ( N = 48) Categories r (ρ) NP total F1. Nursing Care Provision Function F2. Nursing Support Function F3. Communication and Interpersonal Relationship Function NIC total .631 (<.001) .612 (<.001) .583 (<.001) .513 (<.001) F1. Basic ICT use .154 (.296) .088 (.552) .300 (.038) .255 (.081) F2. Nursing Information Utilization and Management .489 (<.001) .462 (.001) .437 (.002) .468 (.001) F3. Professional Responsibility and Ethics .593 (<.001) .562 (<.001) .587 (<.001) .501 (<.001) F4. Use of ICT in Nursing .598 (<.001) .621 (<.001) .473 (.001) .372 (.009) F5. Attitude toward Nursing Informatics .311 (.031) .356 (.013) .163 (.269) .121 (414) 4) Effect of Nursing Informatics Competence on Nursing Performance Regression analysis was conducted to verify the effect of NIC on NP. Before the main regression analysis, diagnostic tests were performed to ensure the validity of the regression model. The Durbin-Watson statistic was 1.830, indicating that there was no autocorrelation. The tolerance value was .651, and the variance inflation factor (VIF) was 1.535, confirming that there was no multicollinearity among the independent variables. Regression analysis results (Table 5) showed that the independent variable, NIC, had a statistically significant positive effect on NP (β=.63, p<.001), and the explanatory power of the model was 38.5%. Table 5. Effect of NIC on NP (N=48) Variables B SE β t or z ( p ) Adjusted R 2 F ( p ) (Constant) 532 .452 1.177 (.245) NIC .875 .159 .631 5.518 (<.001) .385 30.443 (<.001) 5) Comparison of Statistical Analyses between Researcher and AI The comparison of statistical analysis results conducted by the researcher and AI (ChatGPT) is summarized in Table 6. In the descriptive statistics, the mean values for NIC and NP were identical between the AI and the researcher’s analyses. Similarly, both the AI and researcher produced identical results in the reliability analysis and correlation analysis. For the independent t-tests, the results were consistent for most items, although minor differences were observed in some cases. In regression analysis, both the AI and the researcher arrived at identical results. These findings indicate a high level of agreement between AI-based and traditional statistical methods. Table 6. Comparison of Statistical Analysis Results Between Researcher and AI Analysis Type Categories Researcher’s Result AI’s Result Notes (Differences or Similarities) Descriptive Stats NIC Mean (SD) NP Mean (SD) 2.82 (0.45) 2.99 (0.62) 2.82 (0.45) 2.99 (0.62) Identical values Identical values Reliability NIC Cronbach’s α NP Cronbach’s α .88 .98 .88 .98 Identical values Identical values t-test NIC by Nursing Experience NIC by Type pf Hospital NIC by Working Department NIC by NIE NP by Nursing Experience NP by Type pf Hospital NP by Working Department NP by NIE r = .09, p = .527 t = -1.55, p = .129 t = -.22, p = .826 t = .58, p = .566 r = 21, p = .153 t = -1.16, p = .254 t = -1.44, p = .158 t = -1.08, p = .284 r = .09, p = .527 t = -1.54, p = .129 t = -.22, p = .826 t = .58, p = .566 r = 21, p = .153 t = -1.16, p = .253 t = -1.44, p = .158 t = -1.08, p = .284 Identical values Minor difference Identical values Identical values Identical values Minor difference Identical values Identical values Correlation NIC and NP r = .632, p <.001 r = .623, p <.001 Identical values Regression NIC → NP β = .631, p <.001 β = .631, p <.001 Identical values 4. Discussion This study aimed to examine the impact of nursing informatics competence on nursing performance among nurses and to explore the potential application of AI in nursing research by comparing the results of traditional statistical analyses with those generated by AI-based methods. A statistically significant positive correlation was found between NIC and NP, and NIC was shown to have a significant effect on NP (β = .63, p < .001). Furthermore, this research model showed a relatively high explanatory power by explaining 38.5% of the variance in NP. These findings are consistent with the results of a study by Kwak et al.[ 6 ] on the relationship between NIC and work performance among nurses and with the results of a study by Lee et al.[ 7 ](2015) on the influence of NIC on nursing work performance. These findings reaffirm that NIC is a core competence that can contribute to enhancing the efficiency and quality of nursing practice. Among the subdomains of NIC, ‘Use of ICT in Nursing’ demonstrated the strongest correlation with NP. This finding highlights the critical role of effective ICT use in clinical settings as a key factor influencing nursing performance. In today’s digitalized healthcare environment, technologies such as electronic medical records, clinical decision support systems, barcode-based patient and medication management, wearable devices, and the Internet of Things (IoT) are widely implemented [ 9 ]. In highly digitalized healthcare environments, adaptability to new technologies and advanced ICT skills have become core competencies for nurses [ 10 ]. The ability to efficiently integrate and utilize these technologies is now essential for delivering high-quality, safe patient care. In this study, the mean NIC score was 2.82, which is consistent with findings from previous studies using the same instrument, though slightly lower than those reported in some other studies [ 2 , 11 , 12 ]. These differences may be attributed to variations in participants’ characteristics, work environments, and experiences with informatics education. Although the average NIC score varied by institution size and prior exposure to nursing informatics education during undergraduate studies, these differences were not statistically significant. This finding is consistent with some previous research [ 2 ], suggesting that the size of the institution or one-time undergraduate education is not a primary determinant of NIC. Jang [ 2 , 3 ] reported that informatics education experienced within the institution after becoming a nurse has a greater impact on NIC. The results of this study also support the importance of ongoing informatics education in the clinical setting for enhancing NIC. In other words, continuous and systematic informatics training in clinical practice is more crucial for strengthening nurses’ competence than a single educational experience during undergraduate courses. The significance of this study lies in its exploration of the potential for AI utilization, achieved by comparing the results of AI-based statistical analysis with those of traditional statistical analysis conducted by the researchers. Statistical analyses using AI were consistent with the researcher’s results in areas such as descriptive statistics, reliability analysis, and correlation analysis, with only minor differences observed in some independent t-test and regression analysis outcomes. Previous studies on statistical analysis by AI in the field of healthcare research also reported that AI demonstrates high accuracy in data processing, categorization, and tabulation [ 5 ]. It emphasized that the accuracy of inferential statistics compared to expected values can vary depending on the specificity and clarity of the prompt [ 5 ]. These findings suggest that, when using AI-based analysis, both the design of precise prompts and the ability to interpret statistical results accurately are critical for ensuring validity. For researchers with limited statistical knowledge, AI can be a valuable tool to improve analysis accessibility and efficiency. Nevertheless, the potential for errors in interpretating some analytical results underscores the need for researchers to critically review and validate AI-generated outputs. While this study elucidated the impact of NIC on NP, it has certain limitations, including the use of convenience sampling and a relatively small sample size. As a result, the generalizability of these findings is limited. Nevertheless, this study is significant in that it confirmed the positive effect of NIC on NP and explored the potential application of AI in nursing research. 5. Conclusions This study examined the impact of Nursing Informatics Competence (NIC) on Nursing Performance (NP) and investigated the potential for AI integration in nursing research by comparing AI-generated statistical analyses with those performed by a human researcher. The results revealed a significant positive correlation between NIC and NP, indicating that higher levels of competence are associated with better nursing outcomes, with ICT utilization in clinical practice identified as a particularly influential factor. Additionally, the findings showed that AI-based statistical analysis produced results largely consistent with traditional methods, suggesting that AI can serve as a supplementary analytical tool in nursing research, potentially reducing the analytical burden on researchers and improving accessibility to data interpretation. In future research, it will be essential to analyze the role of NIC using large-scale samples that include nurses from diverse regions and healthcare institutions, and to conduct a systematic review of the accuracy and application limitations of AI-based analysis. Abbreviations AI artificial intelligence ICT Information and Communication Technology IoT Internet of Things NIC Nursing Informatics Competence NP Nursing Performance K-NICAS Korean Nursing Informatics Competence Assessment Scale Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of Dongshin University. Participants were given sufficient time to read and fully understand the written explanation before signing the informed consent form. Written informed consent was obtained from all participants before data collection. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No financial disclosure was received. Author' contributions The author performed all aspects of the study and manuscript preparation. Authors' information Seon Mi Jang, PhD, RN Assistant Professor, Department of Nursing, Dongshin University, Naju, Korea. References Canadian Association of Schools of Nursing. Nursing informatics: Entry-to-practice competencies for registered nurses [Internet]. Ottawa: CASN. 2012 [cited 2025 Jul 1]. Available from: https://www.casn.ca/wp-content/uploads/2014/12/Infoway-ETP-comp-FINAL-APPROVED-fixed-SB-copyright-year-added.pdf Jang SM. Data analysis on the factors influencing nursing informatics competence. J Korea Acad Ind Coop Soc. 2022;23(11):535–43. 10.5762/KAIS.2022.23.11.535 . Jang SM. Analysis of research trend related to nursing informatics competence of Korea. J Korea Acad Ind Coop Soc. 2022;23(12):50–7. 10.5762/KAIS.2022.23.12.50 . Park SA, Park KO, Kim SY, Sung YH. A development of standardized nurse performance appraisal tool. Clin Nurs Res. 2007;13(1):197–211. Ruta MR, Gaidici T, Irwin C, Lifshitz J. ChatGPT for univariate statistics: validation of AI-assisted data analysis in healthcare research. J Med Internet Res. 2025;27:e63550. Kwak SY, Kim YS, Lee KJ, Kim MY. Nursing informatics competence, problem-solving ability, and nursing performance of nurses. J Korean Acad Nurs Educ. 2017;23(2):245–54. Lee JM, Kang IS, Yoo SJ. Influence of nursing informatics competence on job satisfaction and nursing performance among nurses. J Health Med Ind. 2015;9(1):51–67. Jang SM, Kim J. Development of nursing informatics competence scale for Korean clinical nurses. CIN: Comput Inf Nurs. 2022;40(10):725–33. 10.1097/CIN.0000000000000934 . Hübner UH, Wilson GM, Morawski TS, Ball MJ, editors. Nursing informatics: a health informatics, interprofessional and global perspective. Cham: Springer Nature; 2022. Havard M, Whistance M, Johns G, Drew S, Cusens C, Thomas S, et al. Defining digital nursing. Br J Nurs. 2024;33(2):72–7. Ko EA, Park JM, Song CE. The impact of the clinical nurse's character and nursing informatics competency on nursing performance. J Korean Clin Nurs Res. 2024;30(2):75–83. Yu M, Kim SY, Ryu JM. Pathway analysis on the effects of nursing informatics competency, nursing care left undone, and nurse-reported quality of care on nursing productivity among clinical nurses. J Korean Acad Nurs. 2023;53(2):236–48. Additional Declarations No competing interests reported. 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Background","content":"\u003cp\u003eHealthcare institutions aim to enhance the efficiency and effectiveness of medical services through digitalization and to realize patient-centered care based on the active participation of both patients and healthcare professionals. In this digital healthcare environment, the work of nurses is becoming increasingly complex, and the importance of nursing informatics competence (NIC) is emphasized to improve nursing performance (NP).\u003c/p\u003e\u003cp\u003eNIC refers to the ability to support the integration of information during nursing practice by utilizing Information and Communication Technology (ICT), while adhering to professional standards and regulations as nurses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the digital healthcare environment, it is natural for nurses to be required to possess NIC, as it is an essential capability for providing safe and high-quality nursing care [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, NIC is closely related to nursing practice, and previous studies have identified it as a significant factor influencing NP [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNP refers to the outcomes of functional tasks that nurses carry out in clinical settings over a specified period [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], encompassing the quality and effectiveness of their professional duties. Recent studies have identified a statistically significant positive correlation between NIC and NP, indicating that nurses with higher informatics competence tend to demonstrate better performance in their roles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Given this relationship, this study aimed to examine the influence of NIC on NP, positioning informatics competence as a key factor in enhancing nursing practice.\u003c/p\u003e\u003cp\u003eIn recent years, Artificial Intelligence (AI) has rapidly gained prominence and is increasingly being applied across diverse fields, including education, research, and clinical practice. AI improves the efficiency and accessibility of exploratory data analysis, making it particularly useful for researchers with statistical insight but limited programming experience [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Accordingly, this study seeks to examine the capability of AI in performing statistical analyses and explore its applicability in nursing research by comparing AI-assisted statistical results with traditional analysis methods. Accordingly, this study aimed to explore the potential of utilizing AI in nursing research by evaluating its statistical analysis capabilities and comparing the results with those obtained through traditional methods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe objectives of this study were to\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e● Assess the level of NIC and identify related factors.\u003c/p\u003e\u003cp\u003e● Determine the impact of NIC on NP.\u003c/p\u003e\u003cp\u003e● Evaluate the performance of AI in statistical analysis by comparing its results with those of traditional methods, to assess accuracy and potential applicability in future nursing research.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e1) Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a descriptive research study conducted to examine the effect of NIC on NP among nurses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participants of this study were general nurses working in clinical settings. The inclusion criteria for the study participants were as follows:\u003c/p\u003e\n\u003cp\u003e- Nurses employed at general hospitals or other hospitals.\u003c/p\u003e\n\u003cp\u003e- General nurses who perform direct patient care in wards, operating rooms, intensive care units, emergency rooms, or outpatient clinics.\u003c/p\u003e\n\u003cp\u003e- Those who understood the purpose of this study and provided informed, voluntary consent to participate.\u003c/p\u003e\n\u003cp\u003eThe sample size was determined using the G*Power 3.1 program, based on linear regression analysis. The independent variable was set as NIC, and the dependent variable as NP. The effect size (f²) was set to 0.30, referencing prior studies [6,7] that reported effect sizes of 0.32 and 0.36 for the relationship between NIC and work performance. The significance level (α) was set at 0.05 and the statistical power (1–β) at 0.80. As a result, the minimum required sample size was calculated to be 29 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3) Research Instruments and Measurement Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Nursing Informatics Competence\u003c/p\u003e\n\u003cp\u003eNursing informatics competence was measured using the Korean Nursing Informatics Competence Assessment Scale (K-NICAS) [8], developed by Jang and Kim. This instrument consists of a total of 20 items, divided into five subdomains: Basic ICT Use (3 items), Utilization and Management of Nursing Information (5 items), Professional Responsibility and Ethics (5 items), Use of ICT in Nursing Practice (4 items), and Attitude Toward Nursing Informatics (3 items). The responses are measured on a 4-point Likert scale (1 to 4), with higher total scores indicating higher levels of NIC. The reliability of the tool was .91 in Cronbach’s α at the time of its development and .88 in this study.\u003c/p\u003e\n\u003cp\u003e(2) Nursing Performance\u003c/p\u003e\n\u003cp\u003eIn this study, nursing performance was measured using the nurse performance appraisal tool developed by Park et al. [4] This instrument consists of a total of 41 items, categorized into three subdomains: Nursing Care Provision Function (29 items), Nursing Support Function (6 items), and Communication and Interpersonal Relationship Function (6 items). Each item is rated on a 5-point Likert scale (0 = not at all, 4 = very well), with higher scores indicating a greater level of NP. The reliability of the tool was .96 in Cronbach’s α at the time of its development and .98 in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4) Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection for this study was conducted in October 2022 using a survey method. The questionnaire consisted of a total of 68 questions to measure the general characteristics of the subjects and research variables. After explaining the purpose and procedure of the study to the nursing department manager at each institution and requesting cooperation, Recruitment notices for study subjects and survey questionnaires were distributed to each ward.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5) Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS version 23 and ChatGPT. The following procedures were performed:\u003c/p\u003e\n\u003cp\u003e(1) Descriptive statistics were calculated to summarize the levels of NIC and NP.\u003c/p\u003e\n\u003cp\u003e(2) Differences in NIC and nursing NP based on the participants’ general characteristics were analyzed using independent t-tests.\u003c/p\u003e\n\u003cp\u003e(3) Pearson’s correlation coefficients were used to analyze the relationship between NIC and NP.\u003c/p\u003e\n\u003cp\u003e(4) Linear regression analysis was conducted to assess the effect of NIC on NP.\u003c/p\u003e\n\u003cp\u003e(5) To evaluate the statistical analysis ability of AI, the same dataset was analyzed using ChatGPT, and the results were compared with those obtained through traditional analysis using SPSS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6) Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of Dongshin University. Prior to completing the questionnaire, participants were provided with a written explanation outlining the purpose of the study, the voluntary nature of participation, assurance of anonymity, and their right to withdraw from the study at any time. Participants were given sufficient time to read and fully understand the written explanation before signing the informed consent form. Written informed consent was obtained from all participants before data collection. Only those who provided written consent participated in the survey. All collected data were anonymized and coded to ensure confidentiality and were used solely for research purposes, and no personal information was collected. Only phone numbers were collected exclusively from those who wished to receive compensation (a mobile coupon) for participating in the study.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e1) Nursing Informatics Competence and Nursing Performance by Participant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 48 participants in this study, the majority were female (89.6%). The mean age was 30.87\u0026plusmn;7.28 years. The average duration of nursing experience was 76.08 months. Regarding the type of institution, 62.5% of the participants were employed at general hospitals, representing the highest proportion. Additionally, 16 nurses (33.3%) received nursing informatics education in undergraduate courses (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. NIC and NP by Participant Characteristics (\u003cem\u003eN\u003c/em\u003e=48)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%) or\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et/F/r (\u003cem\u003e\u0026rho;\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et/F (\u003cem\u003e\u0026rho;\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e43 (89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.84\u0026plusmn;.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.25 (.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.02\u0026plusmn;.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.67 (.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.58\u0026plusmn;.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.82\u0026plusmn;.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30.87\u0026plusmn;7.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003er=.04 (.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003er=.15 (.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNursing Experience (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e76.08\u0026plusmn;89.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003er=.09 (.527)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003er=.21 (.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eAssociate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.38 (.707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.12\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e.82 (.418)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026ge; Bachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e36 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.80\u0026plusmn;.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.95\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eType of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eClinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.69\u0026plusmn;.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.55 (.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1.16 (.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30 (62.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.89\u0026plusmn;.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.08\u0026plusmn;.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003cp\u003eDepartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eWard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.80\u0026plusmn;.48\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.22 (.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1.44 (.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003enon-Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.83\u0026plusmn;.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.11\u0026plusmn;.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003cp\u003eof NIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.87\u0026plusmn;.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.58 (.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2.86\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e-1.08 (.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e32 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.79\u0026plusmn;.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e3.06\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNIE=nursing informatics education\u003c/p\u003e\n\u003cp\u003eBefore analyzing differences in NIC and NP by general characteristics of the participants, tests for normality and homogeneity of variance were conducted. Although the sample size per group did not meet the recommended threshold of 30 for independent t-tests (total participants: 48), the dependent variables were confirmed to follow a normal distribution and demonstrated homogeneity of variance. Therefore, the independent samples t-test was deemed appropriate and applied for group comparisons (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Test of Normality and Homogeneity of Variance for the Dependent Variable NP by Group\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShapiro\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003eWilk (Normality)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevene\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003es Test (Homogeneity)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eW Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF Statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e3.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eAssociate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026ge;Bachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eType of Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eClinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eWorking Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eWard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNon-Ward\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eExperience of NIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.605\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe analysis results showed that the differences in NIC by general characteristics were not statistically significant. Although the average score of NIC for nurses working in general hospitals was higher than that of nurses working in hospital-level institutions, there was no statistically significant difference. Similarly, nurses who had received nursing informatics education in undergraduate courses had higher competence scores compared to those without such education, but this difference also did not reach statistical significance. No significant differences in NP were observed based on the general characteristics of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2) Levels of Nursing Informatics Competence and Nursing Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean score for NIC among the study participants was 2.82 (\u0026plusmn;0.45), with scores ranging from 2.05 to 3.85. Among the subdomains, \u0026lsquo;Attitude toward Nursing Informatics\u0026rsquo; had the highest at 3.44, and \u0026lsquo;Nursing Information Utilization and Management\u0026rsquo; had the lowest at 2.54. The mean score for NP was 2.95 (\u0026plusmn;0.65). For the subdomains, \u0026lsquo;Nursing Support Function\u0026rsquo; showed the highest mean score at 3.15, and \u0026lsquo;Nursing Care Provision Function\u0026rsquo; had the lowest at 2.95(Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Scores of NIC and NP (\u003cem\u003eN\u003c/em\u003e = 48)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 368px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esubdomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003erange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNursing Informatics Competence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 368px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.82\u0026plusmn;.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.05\u0026sim;3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF1. Basic ICT use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.78\u0026plusmn;.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.00\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF2. Nursing Information Utilization and Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.54\u0026plusmn;.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.60\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF3. Professional Responsibility and Ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.88\u0026plusmn;.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.60\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF4. Use of ICT in Nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.65\u0026plusmn;.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.50\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF5. Attitude toward Nursing Informatics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.44\u0026plusmn;.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.00\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNursing Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 368px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.99\u0026plusmn;.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.59\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF1. Nursing Care Provision Function\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.95\u0026plusmn;.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.48\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF2. Nursing Support Function\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.15\u0026plusmn;.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.50\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eF3. Communication and Interpersonal Relationship Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.08\u0026plusmn;.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e1.83\u0026sim;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3) Correlations between Nursing Informatics Competence and Nursing Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA statistically significant positive correlation was found between NIC and NP, with a correlation coefficient of r = .632 (p\u0026lt; .001). Among the subdomains of NIC, \u0026lsquo;Use of ICT in Nursing\u0026rsquo; showed the strongest correlation with overall NP (r=.599, p\u0026lt;.001), and NIC showed the strongest correlation with the \u0026lsquo;Nursing Provision Function\u0026rsquo; subdomain of NP (r=.612, p\u0026lt;.001). Specifically, the \u0026lsquo;Use of ICT in Nursing\u0026rsquo; subdomain of NIC showed the strongest correlation with the \u0026lsquo;Nursing Provision Function\u0026rsquo; subdomain of NP (r=.621, p\u0026lt;.001) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Correlation between NIC and NP (\u003cem\u003eN\u003c/em\u003e = 48)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" rowspan=\"3\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 397px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003er\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(\u0026rho;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 397px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eF1. Nursing Care Provision Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eF2. Nursing Support Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eF3. Communication and Interpersonal Relationship Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.631\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.612\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.583\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.513\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eF1. Basic ICT use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003cp\u003e(.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003cp\u003e(.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.300\u003c/p\u003e\n \u003cp\u003e(.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.255\u003c/p\u003e\n \u003cp\u003e(.081)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eF2. Nursing Information Utilization and Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.489\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.462\u003c/p\u003e\n \u003cp\u003e(.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.437\u003c/p\u003e\n \u003cp\u003e(.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.468\u003c/p\u003e\n \u003cp\u003e(.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eF3. Professional Responsibility and Ethics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.593\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.562\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.587\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.501\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eF4. Use of ICT in Nursing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.598\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.621\u003c/p\u003e\n \u003cp\u003e(\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.473\u003c/p\u003e\n \u003cp\u003e(.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.372\u003c/p\u003e\n \u003cp\u003e(.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp\u003eF5. Attitude toward Nursing Informatics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e.311\u003c/p\u003e\n \u003cp\u003e(.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e.356\u003c/p\u003e\n \u003cp\u003e(.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e.163\u003c/p\u003e\n \u003cp\u003e(.269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.121\u003c/p\u003e\n \u003cp\u003e(414)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4) Effect of Nursing Informatics Competence on Nursing Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegression analysis was conducted to verify the effect of NIC on NP. Before the main regression analysis, diagnostic tests were performed to ensure the validity of the regression model. The Durbin-Watson statistic was 1.830, indicating that there was no autocorrelation. The tolerance value was .651, and the variance inflation factor (VIF) was 1.535, confirming that there was no multicollinearity among the independent variables.\u003c/p\u003e\n\u003cp\u003eRegression analysis results (Table 5) showed that the independent variable, NIC, had a statistically significant positive effect on NP (\u0026beta;=.63, p\u0026lt;.001), and the explanatory power of the model was 38.5%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Effect of NIC on NP (N=48)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et or z (\u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF (\u003cem\u003ep\u003c/em\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e(Constant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1.177 (.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eNIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e5.518 (\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e30.443 (\u0026lt;.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e5) Comparison of Statistical Analyses between Researcher and AI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison of statistical analysis results conducted by the researcher and AI (ChatGPT) is summarized in Table 6. In the descriptive statistics, the mean values for NIC and NP were identical between the AI and the researcher\u0026rsquo;s analyses. Similarly, both the AI and researcher produced identical results in the reliability analysis and correlation analysis. For the independent t-tests, the results were consistent for most items, although minor differences were observed in some cases. In regression analysis, both the AI and the researcher arrived at identical results. These findings indicate a high level of agreement between AI-based and traditional statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Comparison of Statistical Analysis Results Between Researcher and AI\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResearcher\u0026rsquo;s Result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u0026rsquo;s Result\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes (Differences or Similarities)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eDescriptive Stats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNIC Mean (SD)\u003c/p\u003e\n \u003cp\u003eNP Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.82 (0.45)\u003c/p\u003e\n \u003cp\u003e2.99 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.82 (0.45)\u003c/p\u003e\n \u003cp\u003e2.99 (0.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNIC Cronbach\u0026rsquo;s \u0026alpha;\u003c/p\u003e\n \u003cp\u003eNP Cronbach\u0026rsquo;s \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003et-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNIC by Nursing Experience\u003c/p\u003e\n \u003cp\u003eNIC by Type pf Hospital\u003c/p\u003e\n \u003cp\u003eNIC by Working Department\u003c/p\u003e\n \u003cp\u003eNIC by NIE\u003c/p\u003e\n \u003cp\u003eNP by Nursing Experience\u003c/p\u003e\n \u003cp\u003eNP by Type pf Hospital\u003c/p\u003e\n \u003cp\u003eNP by Working Department\u003c/p\u003e\n \u003cp\u003eNP by NIE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003er = .09, p = .527\u003c/p\u003e\n \u003cp\u003et = -1.55, p = .129\u003c/p\u003e\n \u003cp\u003et = -.22, p = .826\u003c/p\u003e\n \u003cp\u003et = .58, p = .566\u003c/p\u003e\n \u003cp\u003er = 21, p = .153\u003c/p\u003e\n \u003cp\u003et = -1.16, p = .254\u003c/p\u003e\n \u003cp\u003et = -1.44, p = .158\u003c/p\u003e\n \u003cp\u003et = -1.08, p = .284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003er = .09, p = .527\u003c/p\u003e\n \u003cp\u003et = -1.54, p = .129\u003c/p\u003e\n \u003cp\u003et = -.22, p = .826\u003c/p\u003e\n \u003cp\u003et = .58, p = .566\u003c/p\u003e\n \u003cp\u003er = 21, p = .153\u003c/p\u003e\n \u003cp\u003et = -1.16, p = .253\u003c/p\u003e\n \u003cp\u003et = -1.44, p = .158\u003c/p\u003e\n \u003cp\u003et = -1.08, p = .284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eMinor difference\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eMinor difference\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eCorrelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNIC and NP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003er = .632, p \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003er = .623, p \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eRegression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eNIC \u0026rarr; NP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026beta; = .631, p \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026beta; = .631, p \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIdentical values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to examine the impact of nursing informatics competence on nursing performance among nurses and to explore the potential application of AI in nursing research by comparing the results of traditional statistical analyses with those generated by AI-based methods.\u003c/p\u003e\u003cp\u003eA statistically significant positive correlation was found between NIC and NP, and NIC was shown to have a significant effect on NP (β\u0026thinsp;=\u0026thinsp;.63, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, this research model showed a relatively high explanatory power by explaining 38.5% of the variance in NP. These findings are consistent with the results of a study by Kwak et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] on the relationship between NIC and work performance among nurses and with the results of a study by Lee et al.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e](2015) on the influence of NIC on nursing work performance. These findings reaffirm that NIC is a core competence that can contribute to enhancing the efficiency and quality of nursing practice.\u003c/p\u003e\u003cp\u003eAmong the subdomains of NIC, \u0026lsquo;Use of ICT in Nursing\u0026rsquo; demonstrated the strongest correlation with NP. This finding highlights the critical role of effective ICT use in clinical settings as a key factor influencing nursing performance. In today\u0026rsquo;s digitalized healthcare environment, technologies such as electronic medical records, clinical decision support systems, barcode-based patient and medication management, wearable devices, and the Internet of Things (IoT) are widely implemented [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In highly digitalized healthcare environments, adaptability to new technologies and advanced ICT skills have become core competencies for nurses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The ability to efficiently integrate and utilize these technologies is now essential for delivering high-quality, safe patient care.\u003c/p\u003e\u003cp\u003eIn this study, the mean NIC score was 2.82, which is consistent with findings from previous studies using the same instrument, though slightly lower than those reported in some other studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These differences may be attributed to variations in participants\u0026rsquo; characteristics, work environments, and experiences with informatics education. Although the average NIC score varied by institution size and prior exposure to nursing informatics education during undergraduate studies, these differences were not statistically significant. This finding is consistent with some previous research [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], suggesting that the size of the institution or one-time undergraduate education is not a primary determinant of NIC. Jang [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] reported that informatics education experienced within the institution after becoming a nurse has a greater impact on NIC. The results of this study also support the importance of ongoing informatics education in the clinical setting for enhancing NIC. In other words, continuous and systematic informatics training in clinical practice is more crucial for strengthening nurses\u0026rsquo; competence than a single educational experience during undergraduate courses.\u003c/p\u003e\u003cp\u003eThe significance of this study lies in its exploration of the potential for AI utilization, achieved by comparing the results of AI-based statistical analysis with those of traditional statistical analysis conducted by the researchers. Statistical analyses using AI were consistent with the researcher\u0026rsquo;s results in areas such as descriptive statistics, reliability analysis, and correlation analysis, with only minor differences observed in some independent t-test and regression analysis outcomes. Previous studies on statistical analysis by AI in the field of healthcare research also reported that AI demonstrates high accuracy in data processing, categorization, and tabulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It emphasized that the accuracy of inferential statistics compared to expected values can vary depending on the specificity and clarity of the prompt [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings suggest that, when using AI-based analysis, both the design of precise prompts and the ability to interpret statistical results accurately are critical for ensuring validity. For researchers with limited statistical knowledge, AI can be a valuable tool to improve analysis accessibility and efficiency. Nevertheless, the potential for errors in interpretating some analytical results underscores the need for researchers to critically review and validate AI-generated outputs.\u003c/p\u003e\u003cp\u003eWhile this study elucidated the impact of NIC on NP, it has certain limitations, including the use of convenience sampling and a relatively small sample size. As a result, the generalizability of these findings is limited. Nevertheless, this study is significant in that it confirmed the positive effect of NIC on NP and explored the potential application of AI in nursing research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study examined the impact of Nursing Informatics Competence (NIC) on Nursing Performance (NP) and investigated the potential for AI integration in nursing research by comparing AI-generated statistical analyses with those performed by a human researcher. The results revealed a significant positive correlation between NIC and NP, indicating that higher levels of competence are associated with better nursing outcomes, with ICT utilization in clinical practice identified as a particularly influential factor. Additionally, the findings showed that AI-based statistical analysis produced results largely consistent with traditional methods, suggesting that AI can serve as a supplementary analytical tool in nursing research, potentially reducing the analytical burden on researchers and improving accessibility to data interpretation. In future research, it will be essential to analyze the role of NIC using large-scale samples that include nurses from diverse regions and healthcare institutions, and to conduct a systematic review of the accuracy and application limitations of AI-based analysis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI artificial intelligence\u003c/p\u003e\n\u003cp\u003eICT Information and Communication Technology\u003c/p\u003e\n\u003cp\u003eIoT Internet of Things\u003c/p\u003e\n\u003cp\u003eNIC Nursing Informatics Competence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNP Nursing Performance\u003c/p\u003e\n\u003cp\u003eK-NICAS Korean Nursing Informatics Competence Assessment Scale\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of Dongshin University. Participants were given sufficient time to read and fully understand the written explanation before signing the informed consent form.\u0026nbsp;Written informed consent was obtained from all participants before data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo financial disclosure was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author performed all aspects of the study and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeon Mi Jang, PhD, RN\u003c/p\u003e\n\u003cp\u003eAssistant Professor, Department of Nursing, Dongshin University, Naju, Korea.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCanadian Association of Schools of Nursing. Nursing informatics: Entry-to-practice competencies for registered nurses [Internet]. Ottawa: CASN. 2012 [cited 2025 Jul 1]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.casn.ca/wp-content/uploads/2014/12/Infoway-ETP-comp-FINAL-APPROVED-fixed-SB-copyright-year-added.pdf\u003c/span\u003e\u003cspan address=\"https://www.casn.ca/wp-content/uploads/2014/12/Infoway-ETP-comp-FINAL-APPROVED-fixed-SB-copyright-year-added.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJang SM. Data analysis on the factors influencing nursing informatics competence. 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J Korean Clin Nurs Res. 2024;30(2):75\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu M, Kim SY, Ryu JM. Pathway analysis on the effects of nursing informatics competency, nursing care left undone, and nurse-reported quality of care on nursing productivity among clinical nurses. J Korean Acad Nurs. 2023;53(2):236\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nursing Informatics, Clinical Competence, Work Performance, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-7167912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7167912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe digitalization of healthcare has highlighted the growing importance of nursing informatics competence, while the use of artificial intelligence in healthcare research continues to expand. This study aimed to examine the impact of nursing informatics competence on nursing performance and to compare the accuracy of statistical analyses conducted by AI and a human researcher.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted with 48 clinical nurses. Nursing informatics competence was measured using the Korean Nursing Informatics Competence Assessment Scale, and nursing performance was assessed with a validated appraisal tool. Data were collected by structured questionnaires and analyzed with SPSS and ChatGPT. Analyses included descriptive statistics, independent t-tests, Pearson\u0026rsquo;s correlation, and linear regression. Results from AI-assisted analyses were compared with those obtained through traditional statistical methods.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eNursing informatics competence showed a significant positive correlation with nursing performance, and regression analysis confirmed it as a significant predictor (β\u0026thinsp;=\u0026thinsp;.63, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), explaining 38.5% of the variance. Among the nursing informatics competence subdomains, \u0026ldquo;Use of ICT in Nursing\u0026rdquo; showed the strongest association with nursing performance. Statistical analyses performed by AI and the human researcher were highly consistent.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eNursing informatics competence is a significant predictor of nursing performance. AI-based statistical analysis showed strong agreement with traditional methods, suggesting its potential as a supplementary tool in nursing research.\u003c/p\u003e","manuscriptTitle":"Nursing Informatics Competence as a Predictor of Nursing Performance: A Comparison of AI and Human Statistical Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 17:34:07","doi":"10.21203/rs.3.rs-7167912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c2af904-08b3-42df-b3e2-f7ab5e35ca0c","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T07:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 17:34:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7167912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7167912","identity":"rs-7167912","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}