What question did this study set out to answer?

The main aim is to translate and validate the FACES tool for assessing cooperation in China's health services.

April 17, 2026Open Access

Translation, reliability, and validity of the chinese face-to-face cooperation evaluation scale short version (FACES) in community health and long-term care providers

Puntos clave

The main aim is to translate and validate the FACES tool for assessing cooperation in China's health services.
Translated and culturally adapted the FACES tool to Chinese.
Assessed psychometric properties with 680 surveyed participants.
Used Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) for structural validity.
Evaluated reliability via Cronbach's alpha, split-half, and Intraclass Correlation Coefficient (ICC).
FACES showed strong internal consistency with a Cronbach’s α of 0.907.
EFA indicated a single factor contributing 65.125% of variance.
CFA demonstrated good model fit with GFI = 0.978 and RMSEA = 0.052.
ICC of 0.982 confirmed excellent test-retest reliability.

Resumen

With China’s rapidly aging population, there is an urgent need to improve collaboration between community health and long-term care providers. However, no validated tool exists to assess the cooperation between these groups in the Chinese context. This study aimed to translate and culturally adapt the FAce-to-face Cooperation Evaluation scale Short version (FACES) into Chinese and assess its psychometric properties among community health and long-term care providers. From July to September 2024, FACES was translated, back-translated, culturally adapted, and reviewed by experts. A total of 680 participants were surveyed to assess the reliability and validity of the Chinese version. Validity was assessed using the content validity index (CVI) to determine content validity, exploratory factor analysis (EFA) (n = 342), and confirmatory factor analysis (CFA) (n = 338) to assess structural validity, and criterion-related validity to determine the ability to correlate with an external criterion. Reliability was assessed using Cronbach’s α coefficient to evaluate internal consistency, split-half reliability to determine scale consistency, and the intraclass correlation coefficient (ICC) to assess test-retest reliability. The Chinese FACES consists of seven items. The I-CVI ranged from 0. 875 to 1. 000, with an S-CVI of 0. 857. EFA revealed a single factor with a cumulative variance contribution of 65. 125%. The factor loading range across all items varied from 0. 640 to 0. 812. CFA showed a satisfactory final model fit: chi-square (χ²) = 26. 539, Goodness of Fit Index (GFI) = 0. 978, Root Mean Squared Error of Approximation (RMSEA) = 0. 052, Comparative Fit Index (CFI) = 0. 989, and Normed Fit Index (NFI) = 0. 978. The Cronbach’s α coefficient was 0. 907, indicating strong internal consistency. Split-half reliability analysis yielded Cronbach’s \: \: values of 0. 905 and 0. 777 for the first four items and last three items, respectively, suggesting acceptable internal consistency. The ICC was 0. 982, demonstrating excellent test-retest reliability. The Chinese version of the FACES demonstrates good reliability and validity, making it suitable for assessing the level of collaboration between community health and long-term care providers in China.

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