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 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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".