Outcomes, Measurement Instruments, and Their Validity Evidence in Randomized Controlled Trials on Virtual, Augmented, and Mixed Reality in Undergraduate Medical Education: Systematic Mapping Review
Bibliographic record
Abstract
BACKGROUND: Extended reality, which encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR), is increasingly used in medical education. Studies assessing the effectiveness of these new educational modalities should measure relevant outcomes using outcome measurement tools with validity evidence. OBJECTIVE: Our aim is to determine the choice of outcomes, measurement instruments, and the use of measurement instruments with validity evidence in randomized controlled trials (RCTs) on the effectiveness of VR, AR, and MR in medical student education. METHODS: We conducted a systematic mapping review. We searched 7 major bibliographic databases from January 1990 to April 2020, and 2 reviewers screened the citations and extracted data independently from the included studies. We report our findings in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Of the 126 retrieved RCTs, 115 (91.3%) were on VR and 11 (8.7%) were on AR. No RCT on MR in medical student education was found. Of the 115 studies on VR, 64 (55.6%) were on VR simulators, 30 (26.1%) on screen-based VR, 9 (7.8%) on VR patient simulations, and 12 (10.4%) on VR serious games. Most studies reported only a single outcome and immediate postintervention assessment data. Skills outcome was the most common outcome reported in studies on VR simulators (97%), VR patient simulations (100%), and AR (73%). Knowledge was the most common outcome reported in studies on screen-based VR (80%) and VR serious games (58%). Less common outcomes included participants' attitudes, satisfaction, cognitive or mental load, learning efficacy, engagement or self-efficacy beliefs, emotional state, competency developed, and patient outcomes. At least one form of validity evidence was found in approximately half of the studies on VR simulators (55%), VR patient simulations (56%), VR serious games (58%), and AR (55%) and in a quarter of the studies on screen-based VR (27%). Most studies used assessment methods that were implemented in a nondigital format, such as paper-based written exercises or in-person assessments where examiners observed performance (72%). CONCLUSIONS: RCTs on VR and AR in medical education report a restricted range of outcomes, mostly skills and knowledge. The studies largely report immediate postintervention outcome data and use assessment methods that are in a nondigital format. Future RCTs should include a broader set of outcomes, report on the validity evidence of the measurement instruments used, and explore the use of assessments that are implemented digitally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".