Research report: Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue

A cross-sectional survey of 404 clinical nurses from four public hospitals in Jiangsu Province, China, found that anxiety about artificial intelligence was strongly associated with intentions to leave their jobs, with occupational fatigue accounting for part of that relationship. Nurses reporting greater AI anxiety also reported more fatigue, while higher emotional intelligence was associated with lower fatigue but did not weaken the specific connection between AI anxiety and fatigue. The study, conducted from March to June 2026 as hospitals underwent digital transformation, was published in Frontiers in Public Health on 22 October 2026.

Because all variables were measured at one point, the results identify statistical associations and a plausible pathway rather than proving that AI anxiety causes fatigue or turnover intention. The researchers recruited frontline registered nurses through convenience sampling from a regional medical consortium that included one tertiary, one secondary and two primary hospitals. Eligible participants had at least one year of clinical experience; interns, trainees, nurse managers, educators, research nurses and administrators were excluded.

Of 446 questionnaires distributed, 404 were retained, giving a valid response rate of 90.58%. Most respondents worked in clinical wards, were aged 25 to 35, held at least a bachelor’s degree and had record-based employment contracts. Turnover-intention scores differed significantly by age, department and professional title, so the researchers adjusted the main structural model for those characteristics.

Four established Chinese-language questionnaires measured the central variables. The 20-item Artificial Intelligence Anxiety Scale assessed concerns spanning learning, substitution of work by AI, socio-technical blind spots and AI configuration. The 35-item Health Workers’ Occupational Fatigue Scale covered physical, cognitive-emotional, social, sleep-related and auditory fatigue.

Turnover intention was measured with six items addressing the desire to leave, motivation to seek another position and perceived opportunities to obtain alternative employment. Emotional intelligence was assessed through the 16-item Wong and Law Emotional Intelligence Scale, which includes self-appraisal, appraisal of others’ emotions, emotion regulation and use of emotion. Internal consistency was strong across the instruments, with Cronbach’s alpha values ranging from 0.818 to 0.966.

The survey data showed substantial correlations among the study’s main measures. AI anxiety and turnover intention had a Pearson correlation of 0.756, while occupational fatigue and turnover intention were correlated at 0.693. AI anxiety was also positively correlated with occupational fatigue at 0.543.

Emotional intelligence had negative correlations with turnover intention, at −0.668, and occupational fatigue, at −0.490, as well as with AI anxiety, at −0.518. The mean AI-anxiety score was 86.98 on a scale ranging from 20 to 140, above its theoretical midpoint, whereas mean occupational fatigue was 97.90 on a scale from 35 to 175, below its theoretical midpoint. Mean turnover intention was 15.55 on a six-item scale with a possible range of 6 to 24.

Structural equation modelling was used to estimate relationships among the latent constructs while controlling for age, department and professional title. The model had acceptable, though not excellent, statistical fit: the chi-square-to-degrees-of-freedom ratio was 4.323, root mean square error of approximation was 0.082, and the comparative fit index was 0.875. AI anxiety was positively associated with occupational fatigue, with a standardized coefficient of 0.611, and with turnover intention, with a coefficient of 0.530.

Occupational fatigue was itself associated with turnover intention at 0.489. All three path coefficients were statistically significant, and the total standardized effect of AI anxiety on turnover intention was 0.829, with a 95% confidence interval from 0.733 to 0.934. Bootstrap testing with 5,000 resamples supported partial statistical mediation by occupational fatigue.

The indirect effect of AI anxiety on turnover intention through fatigue was 0.299, with a bias-corrected 95% confidence interval of 0.129 to 0.379, excluding zero. The direct path from AI anxiety to turnover intention remained significant after occupational fatigue was included, indicating that fatigue did not explain the entire association. In practical terms, the model is consistent with a situation in which AI-related strain is related to thoughts of leaving both through higher fatigue and through some other associated route.

It does not establish that this sequence occurred over time, nor did the researchers directly measure the proposed cognitive, emotional or workload-related resource losses that might explain the association. Emotional intelligence produced a different result. In a moderated regression controlling for age, department and professional title, each additional unit of emotional intelligence was associated with 0.257 lower units of occupational fatigue, a statistically significant negative relationship.

However, the interaction between AI anxiety and emotional intelligence was not significant: its coefficient was 0.043, with a 95% confidence interval of −0.021 to 0.107 and p = 0.187. Adding this interaction increased explained variance by only 0.0017, also with p = 0.187. The overall model explained 60.2% of the variance in occupational fatigue.

Thus, higher emotional intelligence was associated with lower fatigue across the sample, but the study found no evidence that it specifically buffered nurses against fatigue associated with AI anxiety. The distinction between these findings matters for how technology-related psychological strain may be understood. AI anxiety can involve more than discomfort with unfamiliar software.

The scale used in the study captured fears related to learning, job substitution and the changing relationship between people and technical systems. Nurses may also experience uncertainty about professional identity, clinical judgment, autonomy and job security as AI becomes more deeply embedded in records, decision support, infection control and patient services. Under Conservation of Resources theory, concerns about losing valued psychological resources can itself become stressful.

Repeatedly interpreting machine-generated information, adapting routines and learning unfamiliar interfaces may add cognitive and emotional demands, while workflow changes may reduce time available for recovery. The study tested the resulting fatigue pathway statistically; it did not directly measure those proposed mechanisms. The authors interpret the results as suggesting that individual emotional skills alone may be insufficient when technological change reaches professional roles, organizational support and job security.

Their findings support considering AI literacy programs that address both technical competence and role changes, appropriate reliance on AI outputs, ethics, autonomy and uncertainty about future responsibilities. They also identify workflow redesign and fatigue-relief strategies as areas for future intervention research. These suggestions are not direct evidence that such programs will reduce turnover, because the survey did not test an intervention, measure actual resignation or track longitudinal changes.

Nor did it directly measure whether participants regarded AI as a replacement threat or whether they experienced job insecurity. Several limitations constrain how far the findings can be generalized. The cross-sectional design prevents temporal or causal conclusions: anxiety, fatigue and turnover intention were assessed together, and the data cannot show that occupational fatigue preceded or produced thoughts of leaving.

Convenience sampling through hospital communication channels may have introduced selection bias, even though participation was anonymous and supervisors were not directly involved in recruitment. The sample came from one geographic area and a closely integrated hospital consortium, and nurses working in settings outside the sampled institutions or in health systems with different digital-transformation conditions may experience different pressures. The study also lacked objective measures of AI exposure, frequency of use, technology type and implementation stage, limiting interpretation of how directly nurses’ work exposed them to AI.

Longitudinal studies that measure these factors before and after AI implementation are needed to test whether the observed pathway develops over time. The findings nevertheless identify a measurable workforce concern at a time when healthcare organizations are expanding the use of AI. Among the surveyed nurses, AI anxiety was not merely a technology-adoption attitude: it was closely associated with fatigue and stronger turnover intention, with fatigue statistically accounting for part of the connection.

Emotional intelligence was associated with lower fatigue overall but did not specifically weaken the AI-anxiety relationship, pointing toward the possibility that responses to digital transformation must extend beyond individual coping skills. Whether addressing AI-related strain can improve nurse retention, continuity of care or patient safety remains untested in this study. For now, the evidence points to fatigue as a potential intervention point, while also showing why organizational support and role clarity matter in the psychological consequences of healthcare digital transformation.

Subject of Research: Associations among AI anxiety, occupational fatigue, emotional intelligence and nurse turnover intention during healthcare digital transformation

Article Title: Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue

Article References: Zhu, L., Zang, F., Mao, G., Li, X., & Shen, X. (2026). Artificial intelligence (AI) anxiety and nurses’ turnover intention during healthcare digital transformation: the mediating role of occupational fatigue. Frontiers in Public Health, 14, Article 1982743. https://doi.org/10.3389/fpubh.2026.1982743

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1982743

Keywords: AI anxiety, clinical nurses, occupational fatigue, turnover intention, emotional intelligence, healthcare digital transformation, structural equation modelling, nurse retention, cross-sectional survey, Jiangsu hospitals, Artificial, intelligence