Training in medical communication competence through video-based e-learning: How effective are video modeling and video reflection?
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Objective: The present study investigated the efficacy of the didactic approaches of video modeling (VM, best-practice examples), video reflection (VR, problem-based approach), and the combination of both (VMR) in fostering medical communication competence in a video-based digital learning environment. Methods: N = 126 third-year medical students who participated in the pre-post study were assigned to either the intervention groups (VM, VR, and VMR) or the wait-list control group. The efficacy of the three approaches was assessed by means of a situational judgment test (SJT) of medical communication competence. To investigate the differences between the wait-list control and the intervention groups (H1), between the single-mode and combined conditions (H2), and between VM and VR (H3), we applied planned contrast analyses. Results: The planned contrasts showed that the VR condition significantly improved learning outcomes in comparison to the VM condition (H3). The decreased mean scores of the VM condition offset the increased mean scores of VR, and thus no significant differences could be found in H1 and H2. Conclusions: Our study provides promising evidence that VR fosters medical communication competence more effectively than VM. Practical implications: Medical students’ learning in video-based digital environments can be facilitated by the use of interactive VR.
Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 108132 |
Fachzeitschrift | Patient Education and Counseling |
Jahrgang | 121 |
Publikationsstatus | Veröffentlicht - Apr. 2024 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Externe IDs
PubMed | 38184987 |
---|---|
ORCID | /0000-0002-4819-4604/work/170587766 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- E-learning, Medical communication competence, Video modeling, Video reflection