Systematic review of e-learning for surgical training
Bibliographic record
Abstract
BACKGROUND: Internet and software-based platforms (e-learning) have gained popularity as teaching tools in medical education. Despite widespread use, there is limited evidence to support their effectiveness for surgical training. This study sought to evaluate the effectiveness of e-learning as a teaching tool compared with no intervention and other methods of surgical training. METHODS: A systematic literature search of bibliographical databases was performed up to August 2015. Studies were included if they were RCTs assessing the effectiveness of an e-learning platform for teaching any surgical skill, compared with no intervention or another method of training. RESULTS: From 4704 studies screened, 87 were included with 7871 participants enrolled, comprising medical students (52 studies), trainees (51 studies), qualified surgeons (2 studies) and nurses (6 studies). E-learning tools were used for teaching cognitive (71 studies), psychomotor (36 studies) and non-technical (8 studies) skills. Tool features included multimedia (84 studies), interactive learning (60 studies), feedback (27 studies), assessment (26 studies), virtual patients (22 studies), virtual reality environment (11 studies), spaced education (7 studies), community discussions (2 studies) and gaming (2 studies). Overall, e-learning showed either greater or similar effectiveness compared with both no intervention (29 and 4 studies respectively) and non-e-learning interventions (29 and 22 studies respectively). CONCLUSION: Despite significant heterogeneity amongst platforms, e-learning is at least as effective as other methods of training.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".