Introduction: University students encounter multiple academic, psychosocial, and environmental stressors during their transition to higher education. Nursing students may be particularly vulnerable because of concurrent academic and clinical demands. Objective: To identify the relationships among perceived stress, social support, and psychological anxiety among nursing students. Methods: A cross-sectional quantitative study was conducted among first- to fourth-year nursing students at the International University of Ulaanbaatar. After excluding 16 incomplete questionnaires, 119 valid responses were analyzed using descriptive statistics, reliability testing, and correlation analysis in SPSS version 26.
Participants had a mean age of 22.39±5.60 years, and 86.6% were female. The mean item scores were 3.15±0.40 for perceived stress, 4.11±0.78 for social support, and 2.35±0.39 for total anxiety. Perceived stress was positively correlated with total anxiety (r=.310, p=.001), personal anxiety (r=.307, p=.001), and situational anxiety (r=.261, p=.004), and negatively correlated with age (r=-.213, p=.020). Social support was not significantly correlated with perceived stress (r=.015, p=.872) but was negatively correlated with total anxiety (r=-.253, p=.005) and personal anxiety (r=-.334, p<.001). Conclusion Students’ perceived stress was associated with anxiety. Higher social support was related to lower anxiety, although a direct relationship between social support and perceived stress was not observed in this sample.

Introduction: The rapid advancement of Artificial Intelligence (AI) is redefining healthcare delivery, offering significant potential to optimize nursing workflows and minimize administrative burdens in primary care settings. Family Health Centers (FHC) represent the front line of the healthcare system, yet the integration of AI within these facilities remains complex due to varying levels of professional experience and digital literacy among nursing staff. This study was initiated to comprehensively evaluate the current attitudes, future expectations, and perceived structural barriers toward AI adoption among FHC nurses. Furthermore, the research utilizes Marc Prensky’s generational theory to investigate the “digital divide” between different cohorts of healthcare professionals, aiming to provide a theoretical basis for future digital health strategies in community-based nursing. Methods: A cross-sectional analytical study design was employed, involving a representative sample of 205 nurses currently practicing in various Family Health Centers. To ensure statistical power and validity, the minimum required sample size was calculated using G*Power 3.1 software. Data collection was facilitated through a meticulously structured questionnaire designed to capture demographic variables, technological readiness, and specific barriers. All quantitative data were processed and analyzed using IBM SPSS version 25.0. The analytical framework included Chi-square tests for categorical variables and Haberman’s adjusted standardized residual (AR) analysis to identify statistically significant patterns in attitudes across different career stages, ensuring a rigorous evaluation of the research hypotheses. Conclusion The comprehensive analysis confirms that nurses in Family Health Centers demonstrate a predominantly positive and proactive attitude toward AI integration, with a strong emphasis on utilizing these technologies for clinical decision support and patient monitoring. A critical finding of the research is the significant correlation between professional experience and perceived obstacles (p=0.011); while younger “Digital Native” nurses identify infrastructural limitations as primary barriers, experienced “Digital (AR=2.8). Despite these challenges, the remarkably low level of technological distrust (3.9%) suggests a high psychological readiness for digital transformation. To facilitate successful adoption, it is concluded that healthcare organizations must implement tailored digital literacy programs that address the specific needs of each generation, alongside strategic investments in digital infrastructure and nursing curricula.