Effectiveness, merits and challenges of simulation-based online clinical skills teaching compared to face-to-face teaching – a case–control study
Bibliographic record
Abstract
Introduction COVID restrictions saw the migration of the entire teaching–learning process to online mode. Medical educators faced challenges in the execution of skills teaching via online platforms. This study was conducted to evaluate the process and outcome of online skills teaching compared with historical in-person training. Methods Participants were undergraduate medical students during clinical skills training (n = 150). Interventional group students attended online teaching of cardiac and respiratory auscultation via virtual and video demonstrations. The control group was a student cohort from the previous academic year taught face-to-face. Students’ performance was assessed by Objective Structured Practical Examination (OSPE) and compared by the Mann–Whitney U-test. Qualitative data were collected through student surveys and faculty focus groups. Results OSPE scores of the interventional group were lower compared to controls (2.93 vs. 3.75 and 2.76 vs. 3.90) with statistical significance (p < 0.0001*). Positive findings were faculty expression of a sense of accomplishment and students’ satisfaction with staff preparedness, preliminary instructions and time allotment. Faculty expressed a lack of opportunity to provide hands-on training, lesser learner participation and technical issues. Students expressed a lack of confidence, dissatisfaction with interactions and inability to correlate sequences. Discussion We could infer that outcome of online teaching was lower compared to the control reasons that were evident from subjective feedback. The control group had better avenues for interaction, error correction and repetition. Strategies to improve outcomes are small group size, hybrid teaching, faculty training in digital technology and a supportive technical team.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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; 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".