Virtual Reality in Training and Assessment Among Clinical Students and Lecturers at a Nigerian University: A Phenomenological Study
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) technology is increasingly used in health care professionals' education as a novel tool for teaching, learning, and assessment. OBJECTIVE: This study explored the experiences of clinical students and lecturers with VR for training and assessment at a Nigerian institution. It also explored students' perceptions of the usefulness of VR in improving their clinical abilities, knowledge retention, engagement, and overall learning experience. METHODS: A qualitative research study was conducted among 24 clinical students and 8 clinical lecturers. A developed Virtual reality model to TRain and Assess Clinical Students (VTRACS) was used to train and assess clinical students using clinical scenarios. Data were collected through 4 focus group discussions conducted among the clinical students and 8 in-depth interviews conducted among the clinical lecturers. Trustworthiness was maintained, and ethical approval for the study was obtained. The focus group discussions and in-depth interviews were audio-recorded, transcribed verbatim, and analyzed using NVivo (version 11; QSR International). RESULTS: Many of the participants had no previous experience with VR in teaching and learning activities, but judging from their engagement with VTRACS, they defined VR as an alternative learning method (alternative to the traditional physical method). Major themes emerging from the study were expression of excitement, simple and useful innovation, proficiency enhancement, challenges with innovation, and uniformity. The clinical students adjudged VTRACS as an educational supplement with a feeling of unlimited learning access, enhancing clinical abilities while positively impacting their confidence and reducing clinical errors. The participants also described the objectivity and standardization of clinical scenarios as drivers of uniformity in training and assessment of clinical students. The participants were, however, concerned about the loss of empathy with the use of VTRACS, which may negatively impact the affective domain of learning. CONCLUSIONS: The use of VR in the teaching and assessment of clinical students at a Nigerian university is perceived as a complementary method of learning that increases skill acquisition, provides unlimited access to training, and enhances proficiency. While VR is considered to be engaging and beneficial to health care professionals' education, there is a need for its effective incorporation into clinical courses and mitigation of challenges such as cost and technology to ensure the realization of the full potential of VR in health care professionals' education.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".