Medical Students’ Perception of Telesimulation Training: A Qualitative Analysis
Bibliographic record
Abstract
OBJECTIVESOver the past 2 decades, simulation-based learning has become an essential part of medical training. Simulated clinics have proven to be effective for training medical students. Even so, this learning method presents organizational and financial challenges that limit its dissemination to all medical students, especially since the COVID-19 pandemic. Simulated teleconsultation retains the advantages of interactive simulated clinics while offering concrete solutions to the challenges faced. The project aims to explore students’ perspectives on simulated teleconsultation training compared to simulated clinics in person.METHODSTen pre-clerkship students in the Faculty of Medicine at the University of Ottawa participated in interviews following in-person and teleconsultation simulated clinic sessions. The interview guide was developed based on previous work. The questions asked concerned experience with teleconsultation, interaction with the tutor and patient, practical or logistical obstacles, educational value and feasibility. The authors evaluated the results using a thematic analysis.RESULTSThe interview analysis showed that the tutor feedback received during the simulated teleconsultation was comparable to that received after the in-person simulated clinic. Although most of the students enjoy teleconsultation, they raised the challenge of carrying out physical examinations and creating a personal connection with the tutor/patient.CONCLUSIONGiven the circumstances of the pandemic and students’ comfort with technology, the new generation of medical students seems prepared to embrace teleconsultation. The themes identified in the analysis will enable the necessary adjustments to be made in order to optimize their teleconsultation training, an inextricable step in promoting the active offer of healthcare services.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.371 | 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".