Usability of Augmented Reality Technology in Situational Telementorship for Managing Clinical Scenarios: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Telementorship provides a way to maintain the professional skills of isolated rural health care workers. The incorporation of augmented reality (AR) technology into telementoring systems could be used to mentor health care professionals remotely under different clinical situations. OBJECTIVE: This study aims to evaluate the usability of AR technology in telementorship for managing clinical scenarios in a simulation laboratory. METHODS: This study used a quasi-experimental design. Experienced health professionals and novice health practitioners were recruited for the roles of mentors and mentees, respectively, and then trained in the use of the AR setup. In the experiment, each mentee wearing an AR headset was asked to respond to 4 different clinical scenarios: acute coronary syndrome (ACS), acute myocardial infarction (AMI), pneumonia severe reaction to antibiotics (PSRA), and hypoglycemic emergency (HE). Their mentor used a laptop to provide remote guidance, following the treatment protocols developed for each scenario. Rating scales were used to measure the AR's usability, mentorship effectiveness, and mentees' self-confidence and skill performance. RESULTS: A total of 4 mentors and 15 mentees participated in this study. Mentors and mentees were positive about using the AR technology, despite some technical issues and the time required to become familiar with the technology. The positive experience of telementorship was highlighted (mean 4.8, SD 0.414 for mentees and mean of 4.25, SD 0.5 for mentors on the 5-point Likert scale). Mentees' confidence in managing each of the 4 scenarios improved after telementoring (P=.001 for the ACS, AMI, and PSRA scenarios and P=.002 for the HE scenario). Mentees' individual skill performance rates ranged from 98% in the ACS scenario to 97% in the AMI, PSRA, and HE scenarios. CONCLUSIONS: This study provides evidence about the usability of AR technology in telementorship for managing clinical scenarios. The findings suggest the potential for this technology to be used to support health workers in real-world clinical environments and point to new directions of research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".