# Identifying High‐Performance Advanced Practice Profile of Specialist Nurses in Mainland China: A Mixed‐Methods Sequential Explanatory Study

## TL;DR

This study identifies high-performing specialist nurses in China and finds that career ladder and job position are key factors in their success.

## Contribution

The study introduces a mixed-methods approach to classify and understand high-performance profiles in advanced practice nursing in China.

## Key findings

- Three performance profiles were identified: high (26.1%), moderate (46.3%), and low (27.6%).
- Higher professional career ladder and job position were most influential predictors of high performance.
- Leadership support and specialized knowledge were highlighted as key contextual and individual factors.

## Abstract

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.

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.

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 (β = −1.715, p < 0.001). Nurses with higher professional career ladder (e.g., Level 4) were more likely to be in the high‐performance group (β = −1.163, p = 0.042). BPNN analysis identified the professional career ladder and job position as the most influential predictors of high performance. Qualitative findings from interviews with 17 participants reinforced these results, highlighting contextual factors such as leadership support (e.g., formal APN designation, physician endorsement, and organizational recognition) and individual attributes including specialized knowledge, extensive clinical experience, and advanced training.

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.