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The Psychometric Properties and Cutoff Score of the Child and Adolescent Mindfulness Measure (CAMM) in Chinese Primary School Students.

Xin ChenKaixin LiangLiuyue HuangWenlong MuWenjing DongShiyun ChenSi-Tong ChenXinli Chi
Published in: Children (Basel, Switzerland) (2022)
To date, the Child and Adolescent Mindfulness Measure (CAMM) has been translated into several languages, including Chinese. This study aimed to explore the reliability and validity of the Chinese version of the CAMM and to identify the appropriate cutoff score among Chinese primary school students. A total of 1283 participants (52.2% males; 11.52 ± 0.78 years of age) completed a series of questionnaires to evaluate their mental health, including mindfulness, subjective well-being, positive youth development (PYD), depression, and anxiety. Item analysis, Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), criterion-related validity analysis, Receiver Operating Characteristic (ROC) analysis, and reliability analysis were performed. The results show that the Chinese version of the CAMM had acceptable item-scale correlation (r = 0.405-0.775, p < 0.001) and was the best fit for the two-factor ESEM model ( χ 2 = 168.251, p < 0.001, df = 26, TLI = 0.910, CFI = 0.948, RMSEA = 0.065, SRMR = 0.033) among Chinese primary school students. Additionally, the total score of the Chinese version of the CAMM was significantly associated with subjective well-being and PYD (r = 0.287-0.381, p < 0.001), and negatively associated with depression, and anxiety (r = -0.612--0.542, p < 0.001). Moreover, a cutoff score of 22 or higher revealed a significant predictive power for all the included criteria. Finally, the Chinese version of the CAMM had good internal consistency (Cronbach's α = 0.826, McDonald's ω = 0.826). Altogether, the Chinese version of the CAMM had satisfactory psychometric properties, and it can be applied to Chinese children.
Keyphrases
  • psychometric properties
  • mental health
  • young adults
  • sleep quality
  • data analysis