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To examine determinants of nurses' adoption of generative artificial intelligence outputs in clinical practice using a technology acceptance model and an integrated structural equation modelling framework.
Cross-sectional online survey.
Registered nurses in mainland China completed an anonymous questionnaire assessing perceived performance benefits, perceived ease of use, perceived information quality, perceived source credibility, social influence, facilitating conditions, adoption intention and adoption behaviour. Structural equation modelling was used to evaluate the measurement model and estimate a primary mediation model in which perceived performance benefits and perceived ease of use predicted adoption intention, and adoption intention predicted adoption behaviour. An integrated model added information quality, source credibility, social influence and facilitating conditions as additional determinants. Sensitivity analyses were conducted using an ordinal estimator to assess robustness.
The analytic sample comprised 330 nurses. In the primary model, higher perceived performance benefits and greater perceived ease of use were associated with stronger adoption intention, and stronger adoption intention was associated with higher self-reported adoption behaviour. The integrated model showed that perceived information quality contributed to adoption intention beyond core expectancy beliefs, while perceived source credibility showed a small direct association with adoption behaviour. Social influence demonstrated a modest association with adoption intention, whereas facilitating conditions showed weaker associations after accounting for other determinants. Model conclusions were consistent across estimation approaches.
Nurses' adoption of generative artificial intelligence outputs is shaped by perceived performance benefits, ease of use and perceived information quality, with adoption intention functioning as the proximal determinant of self-reported use. Implementation strategies should focus on demonstrable workflow gains, reducing interaction burden and strengthening governance and verification to support safe adoption.
by Chu-Ying Huang, Wen-Pei Chang
BackgroundDigital tools are increasingly widespread in healthcare, particularly in the fields of emotion recognition and mental health assessment.
ObjectivesThis study evaluated whether an artificial intelligence (AI) voice emotion recognition (VER) app could identify nurses’ emotions and explored its associations with their background and health conditions.
MethodsThe emotions of 349 clinical nurses at a medical center in northern Taiwan were analyzed using an AI VER app and several standardized psychological questionnaires. To control for potential confounding variables, demographic and health-related factors including age, gender, work experience, exercise habits, and history of physical symptoms were collected and statistically adjusted in correlation analyses. Convergent validity was tested with Pearson’s correlations, and test-retest reliability was evaluated in 30 nurses using intraclass correlation coefficients (ICCs).
ResultsSignificant correlations were observed between app-derived emotions and standard scales (anger: Novaco Anger Inventory-Short Form, r = .42; fear: Perceived Stress Scale, r = .41; happiness: Oxford Happiness Questionnaire, r = .45; and sadness: Beck Depression Inventory-II, r = .47; all p p = .025), peptic ulcers predicted greater fear (β = .19, p p = .041), and irregular menstrual cycles predicted lower happiness (β = −.13, p = .014) and greater sadness (β = .30, p Conclusion
Peptic ulcers, irregular menstrual cycles, and lack of exercise were associated with negative emotions such as fear, sadness, and anger. The AI VER app could objectively detect these emotional patterns in nurses, helping to identify emotional fluctuations early and support timely mental healthcare.
The study aimed to characterise presenteeism among nurses and identify nurses' presenteeism associated with distinct latent profiles.
This study employed a cross-sectional descriptive approach.
From July to December 2024, data were collected from 404 Chinese clinical nurses across four tertiary hospitals in Sichuan Province, Southwest China, using demographic questionnaires, the Stanford Presenteeism Scale (SPS-6), and the Challenge- and Hindrance-Related Self-Reported Stress Scale (C-HSS). A latent profile analysis was conducted on SPS-6 scores using Mplus 8.3, followed by univariate analyses to compare characteristics across subgroups.
The total mean score of nurses' presenteeism is (16.13 ± 4.46), with approximately 59.4% classified as having a high level of presenteeism. Four latent profiles of nurses' presenteeism were identified through LPA: low fatigue–low work constraint (19.8%), low fatigue–high work constraint (33.9%), high fatigue–low work constraint (18.8%), and high fatigue–high work constraint (27.5%).
Nurses demonstrated moderately severe presenteeism, with LPA revealing four distinct phenotypes characterised by divergent fatigue– work constraint configurations. This heterogeneity underscores the need for stratified interventions addressing unique risk profiles across subgroups. Administrators should adopt targeted interventions according to the characteristics of nurses in different profiles to minimise nurses' loss of productivity.
This study addresses the evidence gap regarding the significant heterogeneity of presenteeism among nurses and the lack of precise identification, and identifies four distinct latent profiles of presenteeism. The findings provide critical evidence for nursing managers to design and implement differentiated intervention strategies tailored to groups with different risk characteristics.
The study followed the STROBE guideline.
This study did not include patient or public involvement in its design, conduct or reporting.
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