A MULTIMODAL APPROACH TO WORKING ALLIANCE DETECTION IN THERAPIST-PATIENT PSYCHOTHERAPY USING DEEP LEARNING MODELS
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Salah, Albert | |
| dc.contributor.author | Vollebregt, Rivka | |
| dc.date.accessioned | 2023-10-03T00:01:00Z | |
| dc.date.available | 2023-10-03T00:01:00Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/45323 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | The purpose of this research was to gain a better understanding of the working alliance so therapists can get more insight into what factors patients take into account in their perception of it, and so working alliance might be predicted auto- matically to detect a low working alliance early in the therapeutic process. In this study, we analyzed the relationship between various indicators across multiple modalities and the perception of working alliance. | |
| dc.title | A MULTIMODAL APPROACH TO WORKING ALLIANCE DETECTION IN THERAPIST-PATIENT PSYCHOTHERAPY USING DEEP LEARNING MODELS | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Artificial Intelligence | |
| dc.thesis.id | 24957 |