Lijun Yao
Biographic Data
| ID | 7958168 |
|---|---|
| NAME | Lijun Yao |
| GIVEN NAMES | Lijun |
| FAMILY NAME | Yao |
| SIGNATURE | YAO L |
| AFFILIATIONS | Tongji University |
| ORCID | 0000-0002-7491-0045 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning
This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes
Influencing Factors and Machine Learning-Based Prediction of Side Effects in Psychotherapy
Background: Side effects in psychotherapy are a common phenomenon, but due to insufficient understanding of the relevant predictors of side effects in psychotherapy, many psychotherapists or clinicians fail to identify and manage these side effects. The purpose of this study was to predict whether clients or patients would experience side effects in psychotherapy by machine learning and to analyze the related influencing factors. Methods: A self-…
No prominent works on this page.
Influencing Factors and Machine Learning-Based Prediction of Side Effects in Psychotherapy
Background: Side effects in psychotherapy are a common phenomenon, but due to insufficient understanding of the relevant predictors of side effects in psychotherapy, many psychotherapists or clinicians fail to identify and manage these side effects. The purpose of this study was to predict whether clients or patients would experience side effects in psychotherapy by machine learning and to analyze the related influencing factors. Methods: A self-…
Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning
This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes
Mental Health Research Topics (2 works) · Personality Disorders and Psychopathology (2 works) · Psychology (2 works) · Psychotherapist (2 works) · Anger (1 works) · Anxiety (1 works) · Artificial Intelligence (1 works) · Clinical Psychology (1 works) · Clinical Psychology (1 works) · Computer Science (1 works)