Bowen Wang

Ethics remediation assessments play a critical role in determining professional readiness to return to safe practice following disciplinary violations. This study offers a comprehensive psychometric evaluation of the Ethics and Boundaries Assessment Services (EBAS) instrumental, a structured ethics assessment widely used by licensing boards across healthcare. A sample of 562 completed constructed-response items across five core domains: Boundaries, Fraud, Professional Standards, Substance Abuse, and Unprofessional Conduct. Responses were scored using a four-point rubric by trained raters. Reliability, validity, and fairness were examined using multiple approaches, including interrater reliability via intraclass correlation coefficients (ICCs), construct validity through multitrait – multimethod (MTMM) analysis, confirmatory factor analysis (CFA), and item-level functioning through Graded Response IRT modeling. Results supported strong internal consistency, acceptable interrater reliability, and a theoretically coherent factor structure. These findings provide initial empirical support for the EBAS instrument as a defensible and psychometrically sound tool in high-stakes regulatory settings.

Tactile emotion recognition provides a lot of valuable information in human-computer interaction, and it has strong application prospects in many aspects such as smart home and medical treatment. So this situation raises a question: How to quickly and efficiently let the robot perform the correct emotion recognition? In this work, we develop a lifelong learning algorithm which is based on the efficient dictionary learning technology, to tackle the tactile emotion recognition across different tasks. To verify the efficiency of the proposed method, we applied it to two data sets for experimentation: Corpus of Social Touch (CoST) and our dataset(We built it with a 12X12 array sensor). The results show that the proposed lifelong learning algorithm achieves satisfactory results.