Simulation-Based Teaching of Telemedicine for Future Users of Teleconsultation and Tele-Expertise: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Health care professionals worldwide are increasingly using telemedicine in their daily clinical practice. However, there is still a lack of dedicated education and training even though it is needed to improve the quality of the diverse range of telemedicine activities. Simulation-based training may be a useful tool in telemedicine education and training delivery. OBJECTIVE: This study aims to assess the feasibility and acceptability of simulation-based telemedicine training. METHODS: We assessed five telemedicine training sessions conducted in a simulation laboratory. The training was focused on video teleconsultations between a patient and a health care professional. The assessment included the participants' satisfaction and attitudes toward the training. RESULTS: We included 29 participants in total. Participant satisfaction was high (mean score 4.9 of 5), and those that took part stated the high applicability of the simulation-based training to their telemedicine practices (mean score 4.6 of 5). They also stated that they intended to use telemedicine in the future (mean score 4.5 of 5). CONCLUSIONS: Simulation-based training of telemedicine dedicated to video teleconsultation was feasible and showed high satisfaction from participants. However, it remains difficult to scale for a high number of health care professionals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".