Measuring motivation for medical treatment: Confirming the factor structure of the Achievement Motivation Index for Medical Treatment (AMI-MeT)

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Abstract

Background: Developments in chemotherapy have led to changes in cancer care in Japan, with the government promoting a transition to outpatient chemotherapy. This requires patients and their families to participate more actively in treatment than in the past. However, it remains unclear how patients' motivation for medical treatment affects clinical consultations with their physicians. To investigate this, we developed a psychological index called the Achievement Motive Index for Medical Treatment (AMI-MeT), which comprises self-derived achievement motivation (AMS) and achievement motivation derived from others (AMO). However, its factor structure has not yet been confirmed in populations other than healthy university students. Thus, the aims of this study were to confirm the factor structure of the AMI-MeT in other groups and to determine the convergent and divergent validity of the AMI-MeT. Methods: The AMI-MeT was administered to university students (n = 414), apparently healthy workers (n = 154), and cancer patients (n = 51). Multi-group confirmatory factor analysis was conducted and the mean scores of the AMI-MeT were compared between the groups. Correlations between the AMI-MeT and the Self-Construal Scale, comprising independent self-construal (IndSC) and interdependent self-construal (InterSC) subscales, were investigated in another group of students (n = 335). Results: The multi-group confirmatory factor analysis supported a two-factor structure of the AMI-MeT: The weak invariance model was the best fit for the data. The mean scores of the AMI-MeT in apparently healthy workers and cancer patients were significantly higher than that in students (P

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Hatta, T., Naria, K., Yanagihara, K., Ishiguro, H., Murayama, T., & Yokode, M. (2016). Measuring motivation for medical treatment: Confirming the factor structure of the Achievement Motivation Index for Medical Treatment (AMI-MeT). BMC Medical Informatics and Decision Making, 16(1). https://doi.org/10.1186/s12911-016-0260-0

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