The mental state inferences in healthcare professionals scale: a psychometric study

Autor: Moisés Betancort, Naira Delgado, Enrique García-Marco, María Dolores Morera, Elena Lorenzo, Lasana T. Harris
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Psychology, Vol 12, Iss 1, Pp 1-9 (2024)
Druh dokumentu: article
ISSN: 2050-7283
DOI: 10.1186/s40359-024-02119-4
Popis: Abstract Background Empathizing with patients is an essential component of effective clinical care. Yet, a debate persists regarding how healthcare professionals’ emotions and performance are impacted when they engage in empathetic behaviors and attempt to discern patients’ mental states during clinical interactions. To approach this issue, this study explores the psychometric properties of the Mental State Inferences in Healthcare Professionals Scale (MSIHPS), a novel eight-item scale to evaluate healthcare professionals’ perceptions of their own disposition to infer patients’ mental states during clinical interactions. Method The study was conducted across various units within a regional hospital and primary care units affiliated with the Canarian Public Health Service in Spain. Data collection took place over the course of 2022, spanning from February to November. The psychometric properties of the scale were analyzed, including an exploratory and a confirmatory factor analysis, to test reliability and validity. Additionally, an item response model was run to test potentially biased items. The study collected data from a sample of 585 healthcare professionals. Results Overall, the results indicate that the psychometric properties of the tool are adequate. Furthermore, the unidimensionality of the scale was confirmed using the item response model, wherein the eight-items significantly contribute to predicting the latent construct. Conclusion The MSIHPS offers the opportunity to explore the role of mentalizing in a diversity of healthcare settings. This measure can be useful to explore the relationship between healthcare professionals’ disposition to infer patients’ mental states and other relevant variables in clinical interactions, such as empathy and clinical performance.
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