Objectives: The current study aims to identify individual and joint drivers that significantly influence the safety climate in healthcare industries by using Bayesian network (BN) simulations for an in-depth analysis.
Methods: Survey data were collected from 452 employees from two branches of one hospital in China for a study about workplace safety. The original English surveys were translated into Chinese using the back-translation procedure recommended by Brislin. Employees were asked to complete two online surveys with 1 month in between each administration. The sample was 42% doctors and 58% nurses. A BN model, based on theory, was updated and complemented with expert knowledge. A graphical model based on expert knowledge and data-driven machine learning approaches was used to refine the BN structure, representing interrelationships among all studied variables. The BN model was employed to identify the best key drivers and joint strategies for safety climate improvement.
Results: The BN model demonstrated a good overall fit. The Euclidean distance metric was used to assess the influence between connected variables, with interpersonal trust and locus of control having the strongest independent effects on safety climate among the five contributing factors. Joint strategies, particularly joint optimization of error disclosure culture and interpersonal trust, as well as error disclosure culture and self-efficacy, were most effective in promoting a safe climate.
Conclusions: The findings suggest that hospital safety climate can be improved by providing a psychologically safe error disclosure culture and enhancing interpersonal trust among employees and their self-efficacy.
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