Educational Effectiveness of Telementoring as a Continuing Professional Development Intervention for Surgeons in Practice: A Systematic Review
Bibliographic record
Abstract
Objective: We performed a systematic review to determine the educational effectiveness of telementoring as a continuing professional development (CPD) intervention for surgeons in practice. Background: Surgeons can mentor their peers in remote locations using videoconferencing communication, referred to as telementoring. Methods: We searched MEDLINE and EMBASE and included studies assessing the educational effectiveness of telementoring interventions used by surgeons in practice. We excluded studies involving only trainees and those not evaluating educational effectiveness. Two reviewers independently screened, extracted data, and assessed study quality using the Medical Education Research Study Quality Instrument (MERSQI; maximum score 18). Educational outcomes were categorized using Moore's Outcomes Framework. Results: We retrieved a total of 1351 records, and 252 studies were selected for full-text review. Twenty-eight studies were included with 1 randomized controlled trial, 19 cohort studies, 5 qualitative studies, and 3 case studies, totaling 178 surgeons and 499 cases. The average MERSQI score was 10.21 ± 2.2 out of 18. Educational outcomes included surgeons' satisfaction with telementoring interventions (Moore's Level 2) in 12 studies, improvement in surgeons' procedural knowledge (Level 3b) in 3 studies, improvements in surgeons' procedural competence in an educational setting (Level 4) in 4 studies, performance in a workplace-based setting (Level 5) in 23 studies, and patient outcomes (Level 6) in 3 studies. No studies reported community health outcomes (Level 7). Conclusions: Moderate-level evidence demonstrates the use of telementoring as effective in changing surgeons' knowledge and competence in both educational and workplace-based settings. Its use is also associated with changes in patient outcomes.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".