Fall risk prediction ability in rehabilitation professionals: structural equation modeling using time pressure test data for Kiken-Yochi Training

Abstract

Background Falls occur frequently during rehabilitation for people with disabilities. Fall risk prediction ability (FRPA) is necessary to prevent falls and provide safe, high-quality programs. In Japan, Kiken Yochi Training (KYT) has been introduced to provide training to improve this ability. Time Pressure-KYT (TP-KYT) is an FRPA measurement specific to fall risks faced by rehabilitation professionals. However, it is unclear which FRPA factors are measured by the TP-KYT; as this score reflects clinical experience, a model can be hypothesized where differences between rehabilitation professionals (licensed) and students (not licensed) can be measured by this tool. Aims To identify the FRPA factors included in the TP-KYT and verify the FRPA factor model based the participants’ license status. Methods A total of 402 participants, with 184 rehabilitation professionals (physical and occupational therapists) working in 12 medical facilities and three nursing homes, and 218 rehabilitation students (physical and occupational therapy students) from two schools participated in this study. Participant characteristics (age, gender, job role, and years of experience and education) and TP-KYT scores were collected. The 24 TP-KYT items were qualitatively analyzed using an inductive approach based on content, and FRPA factors were extracted. Next, the correction score (acquisition score/full score: 0–1) was calculated for each extracted factor, and an observation variable for the job role (rehabilitation professional = 1, rehabilitation student = 0) was set. To verify the FRPA factors associated with having or not having a rehabilitation professional license, FRPA as a latent variable and the correction score of factors as an observed variable were set, and structural equation modeling was performed by drawing a path from the job role to FRPA. Results The results of the qualitative analysis aggregated patient ability (PA), physical environment (PE), and human environment (HE) as factors. The standardized coefficients of the model for participants with or without a rehabilitation professional license and FRPA were 0.85 (p < 0.001) for FRPA from job role, 0.58 for PA, 0.64 for PE, and 0.46 for HE from FRPA to each factor (p < 0.001). The model showed a good fit, with root mean square error of approximation < 0.001, goodness of fit index (GFI) = 0.998, and adjusted GFI = 0.990. Conclusion Of the three factors, PA and PE were common components of clinical practice guidelines for fall risk assessment, while HE was a distinctive component. The model’s goodness of fit, which comprised three FRPA factors based on whether participants did or did not have rehabilitation professional licenses, was good. The system suggested that rehabilitation professionals had a higher FRPA than students, comprising three factors. To provide safe and high-quality rehabilitation for patients, professional training to increase FRPA should incorporate the three factors into program content.