Adaptation and Validation of a Brief Artificial Intelligence Job Performance Scale (BAIJPS) in Nurses
DOI:
https://doi.org/10.69653/iah20232Keywords:
nurses, performance, artificial intelligence, validationAbstract
Background: The integration of artificial intelligence (AI) in healthcare is revolutionizing work practices and improving medical care through technologies such as decision support systems and surgical robots. However, it faces challenges such as cost, accessibility, and the need for specialized training. Objective: This study aimed to adapt the Brief Job Performance Scale (BJPS) and evaluate the psychometric properties of the Brief Artificial Intelligence Job Performance Scale (BAIJPS) in Peruvian nurses, considering the specific demands and integration of AI in their work practices. Methods: An instrumental design with convenience sampling was employed, including 199 nurses (M=35.27, SD=8.5). Analyses included descriptive statistics, Confirmatory Factor Analysis (CFA), and measurement invariance. Results: The unidimensional factorial structure of the scale showed a good fit (CFI = 0.97, TLI = 0.95, RMSEA = 0.08, SRMR = 0.03), with high factor loadings and internal consistency (α = 0.96, ω = 0.96). Measurement invariance by gender confirmed that the BAIJPS is applicable equally among men and women. Conclusions: The BAIJPS is a valid and reliable tool for assessing job performance in nurses in the context of AI integration, reflecting both task and contextual performance. This supports the implementation of policies to improve training and adaptation of nurses to the use of AI technologies, ensuring accurate and culturally relevant measurements.
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