Development of Therapeutic Competencies in Health Care Students: Qualitative Focus Group Study Using 360-Degree Video and Virtual Reality Technology
Bibliographic record
Abstract
BACKGROUND: Therapeutic competence is a critical skill for health care professionals, encompassing communication, interaction, and guidance in vulnerable situations. Virtual reality (VR) and 360-degree video technologies have emerged as innovative tools in health care education, offering immersive and interactive learning experiences. However, there is limited research on their effectiveness in developing therapeutic competencies among health care students. OBJECTIVE: This pilot study aimed to explore the feasibility, usability, and perceived educational value of a virtual learning resource using VR and 360-degree video to enhance therapeutic competence in health care students. METHODS: A virtual learning resource was developed, consisting of three modules: (1) a virtual home visit, (2) observation of therapeutic conversations using a 360-degree video, and (3) practice of therapeutic conversations in a simulated environment using VR. The resource was tested with students (n=12) from occupational therapy, psychology, and dentistry programs. Data were collected through focus group interviews after the students completed the modules. Thematic analysis was conducted to identify key themes related to the educational value and learning outcomes of the resource. RESULTS: The analysis revealed four key themes: (1) active exploration, where students engaged deeply with the material and contextualized theoretical knowledge; (2) observation, which provided practical insights into therapeutic conversations; (3) practice and reflection, which allowed students to refine their skills and build confidence; and (4) translation of theoretical knowledge into practical skills. Students reported that the resource was engaging, immersive, and effective in promoting learning compared to traditional teaching methods. Some students found the VR experience intense but valuable for skill development. CONCLUSIONS: This pilot study demonstrates the feasibility and potential educational value of integrating VR and 360-degree video into health care education. The findings provide preliminary insights into the resource's ability to enhance therapeutic competence and student engagement. Future research should focus on larger, multi-institutional studies to validate these findings and assess the resource's impact on measurable learning outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".