ArticleFrontiers in public health2026
Artificial intelligence anxiety and its association with willingness to receive AI-related training among maternal and child health nurses: a multicenter network analysis.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rapid integration of artificial intelligence (AI) into maternal and child healthcare introduces unique psychological challenges for nursing staff. This study aimed to explore the network structure of AI anxiety among Chinese maternal and child health nurses and examine its associations with willingness to receive AI-related training, thereby generating preliminary implications for organizational support strategies. Methods: A multicenter cross-sectional survey was conducted between May and August 2025, enrolling 848 nurses from 46 maternal and child healthcare institutions across 26 Chinese provinces. AI anxiety was assessed using the Artificial Intelligence Anxiety Scale (AIAS), and willingness to receive AI-related training was assessed using a single 10-point item. Network analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) and the Extended Bayesian Information Criterion (EBIC) was performed to estimate statistically central nodes, cross-dimensional bridge connectivity, and node predictability. Results: The highest strength centrality values were observed for a humanoid-AI-fear item and a learning-anxiety item. AI misuse concerns and job replacement fears showed the highest bridge-strength values, indicating relatively strong cross-dimensional connectivity among the anxiety dimensions. Notably, learning-anxiety items showed the strongest positive associations with willingness to receive AI-related training, with edge weights ranging from 0.22 to 0.24. The estimated strength and bridge-strength rankings showed acceptable stability, with correlation-stability coefficients of 0.594 and 0.750, respectively, while node predictability ranged from 69.3 to 92.1%. Conclusion: AI anxiety among maternal and child health nurses showed a multifaceted network structure in which learning-related stress and fear of anthropomorphic AI were statistically prominent. These data generate preliminary hypotheses about organizational support but do not identify effective intervention targets. Because willingness to receive AI-related training was assessed using a single global item, the study does not identify nurses' multidimensional educational needs, preferred content or delivery modalities, competency priorities, or perceived barriers. Literature-informed options such as phased or simulation-based training should therefore undergo needs assessment and longitudinal or interventional evaluation before implementation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.