Effectiveness of Telementoring in Surgery Compared With On-site Mentoring: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Mentorship is important but may not be feasible for distance learning. To bridge this gap, telementoring has emerged. The purpose of this systematic review was to evaluate the effectiveness of telementoring compared with on-site mentoring. METHODS: A search was done up to March 2015. Studies were included if they used telementoring between surgeons during a clinical encounter and if they compared on-site mentoring and telementoring. RESULTS: A total of 11 studies were included. All reported no difference in complication rates, and 9 (82%) reported similar operative times; 4 (36%) reported technical issues, which was 3% of the total number of cases in the 11 studies. No study reported on higher levels of evidence for effectiveness of telementoring as an educational intervention. CONCLUSION: Studies reported that telementoring is associated with similar complication rates and operative times compared with on-site mentoring. However, the level of evidence to support the effectiveness of telementoring as a training tool is limited. There is a need for studies that provide evidence for the equivalence of the effectiveness of telementoring as an educational intervention in comparison with on-site mentoring.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".