ArticleJournal of nursing management2026
Identifying High-Performance Advanced Practice Profile of Specialist Nurses in Mainland China: A Mixed-Methods Sequential Explanatory Study.
Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Identifying High-Performance Advanced Practice Profile of Specialist Nurses in Mainland China: A Mixed-Methods Sequential Explanatory Study.Journal of nursing management · 2026Article
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Authors and funding
6 authors.
Funding
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Abstract
Objectives: Identifying high-performing advanced practice nursing roles and understanding the factors that contribute to their effectiveness are critical for advancing professional development, optimizing workforce deployment, and ensuring long-term sustainability in nursing. This study aimed to (1) identify distinct latent profiles of advanced practice nursing among specialist nurses in mainland China, (2) quantitatively examine the individual and contextual factors associated with high performance, as characterized by these profiles, and (3) qualitatively confirm the significant factors using explanatory semistructured interviews in the high-performance groups. Methods: A mixed-methods sequential explanatory design was used, in which quantitative data were collected first and subsequently explained through qualitative interviews. Certified specialist nurses from 16 hospitals across urban and rural areas of Shanghai were included. Latent profile analysis (LPA) was conducted using the five domains from the Advanced Practice Role Delineation tool as manifest indicators to classify nurses into distinct performance profiles. Multinomial logistic regression was used to examine potential determinants (e.g., job position) of group membership. Additionally, a backpropagation neural network (BPNN) was developed to rank the importance of contributing factors. Specialist nurses identified as high performers in the quantitative phase were purposively sampled for explanatory semistructured qualitative interviews. Results: Three latent profiles emerged: high performance (26.1%), moderate performance (46.3%), and low performance (27.6%). Compared to APNs, staff nurses had significantly lower odds of belonging to the high-performance group ( Conclusion: Identifying the profiles of advanced practice nursing roles provides valuable insights for optimizing APN performance and informing targeted management and policy strategies. High-performing specialist nurses are positioned at the nexus of individual capability, interdisciplinary collaboration, and institutional support.
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