Login / Signup

Rasch Analysis of Self-Reported Adherence to Patient-Centered Physical Therapy Scale among Japanese Physical Therapists: Cross-Sectional Study.

Hiroshi Takasaki
Published in: International journal of environmental research and public health (2021)
This study primarily aimed to develop a shorter version of the self-reported adherence to patient-centered physical therapy (s-SAPCPTS) by using Rasch analysis and secondarily aimed to preliminarily investigate the relationship between the s-SAPCPTS scores and demographics (i.e., age, sex, final academic degree (non-postgraduate degrees or postgraduate degrees), and practice environment). In an online anonymous survey, 110 Japanese physical therapists completed the self-reported adherence to patient-centered physical therapy and provided data on their demographics. Through the Rasch analysis, items were excluded in a stepwise manner, until certain pre-established criteria of the unidimensionality were satisfied. Subsequently, a conversion table for the Rasch score was developed. Furthermore, multiple regression analysis was conducted by using the independent variables age, sex, and final academic degree. Using the Kruskal-Wallis test, we compared the Rasch s-SAPCPTS scores among four practice environments. Consequently, the seven-item s-SAPCPTS was developed by excluding seven items through the Rasch analysis. Postgraduate degree was a statistically significant contributing factor for Rasch s-SAPCPTS scores (p = 0.038, β = 0.20). The Kruskal-Wallis test demonstrated statistically significant differences in the Rasch s-SAPCPTS scores among the four practice environments (p = 0.006). In conclusion, the seven-item s-SAPCPTS was developed with the preliminary evidence of construct validity. It was also found that the final academic degree and practice environment could be the contributing factors of s-SAPCPTS scores.
Keyphrases
  • psychometric properties
  • primary care
  • healthcare
  • mental health
  • physical activity
  • metabolic syndrome
  • insulin resistance
  • skeletal muscle
  • artificial intelligence
  • big data
  • data analysis