'Virtual Mentorship is a No-Brainer': The Application of a Virtual Mentorship Programme for Prospective Plastic Surgery Trainees
Bibliographic record
Abstract
Aims This study aimed to evaluate the effectiveness of a virtual mentorship programme in plastic surgery designed for medical students and foundation doctors in the United Kingdom. The programme sought to enhance understanding of common and emergency conditions, provide guidance on the application process for speciality training, and facilitate networking opportunities. Materials and methods The programme consisted of six sessions delivered via Microsoft Teams (Microsoft® Corporation, Redmond, WA) over a four-month period from May to August 2024. Participants completed online pre- and post-mentoring questionnaires. Wilcoxon signed-rank test was used to compare paired data responses. Results Ten participants completed both questionnaires; 90% were medical students, and 10% were foundation-year doctors. There was a significant increase in the understanding of common plastic surgery conditions and emergencies (p < 0.05), as well as improved knowledge of the application processes for core surgical training (p < 0.05) and higher speciality training (p < 0.05). Interest in the speciality significantly increased (p < 0.05), and participants were more likely to seek in-person mentorship (p < 0.05). The programme was well-received, with 100% rating it as 'excellent' or 'very good'. Conclusions The virtual mentorship programme effectively enhanced foundational knowledge, career preparation, and mentor-mentee relationships. Its implementation is recommended both alone and in combination with traditional face-to-face mentorship.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".