Surgical Trainee Opinions in the United Kingdom Regarding a Three-Dimensional Virtual Mentoring Environment (MentorSL) in Second Life: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Medical mentoring is becoming increasingly complex with the evolving needs of trainees and the complexities of their personal and social lives. The Internet is an enabling technology, which increasingly facilitates interaction with multiple people at a distance. Web 2.0 and 3.0 technology shows promise in furthering this facilitation. OBJECTIVE: The objective of our study was to establish opinions among doctors in postgraduate surgical training regarding mentoring and whether these doctors would readily accept virtual mentoring following a brief experience. METHODS: On the 12th of February 2012, an introductory teaching class was arranged by The London Postgraduate School of Surgery for doctors in training. Participants were introduced to a novel virtual mentoring system and asked to complete a questionnaire regarding their opinions before and after the demonstration. RESULTS: A total of 57 junior doctors attended. Among them, 35 completed questionnaires pre- and postdemonstration. Regarding usefulness of a 3D virtual environment for mentoring, 6/35 (17%) agreed or strongly agreed and 20/35 (57%) were unsure prior to the session. Following 20 minutes using MentorSL, this significantly increased to 14/35 (40%) agreeing or strongly agreeing with 11/35 (31%) unsure (P<.001). Prior to using MentorSL, regarding usefulness of voice communication for virtual mentoring, 11/35 (31%) agreed or strongly agreed and 18/35 (51%) were unsure. Following 20 minutes using MentorSL, 19/35 (54%) agreed or strongly agreed and 10/35 (29%) were unsure of usefulness. Regarding ease of use of navigation, search mentor, meeting scheduling, and voice communication features, 17/35 (49%), 13/35 (37%), 15/35 (43%), and 16/35 (46%) participants agreed or strongly agreed, respectively. Regarding usefulness of telementoring, 24/35 (69%) agreed or strongly agreed, increasing to 28/35 (80%) following the introduction. For usefulness of multiple mentors, initially 24/35 (69%) agreed or strongly agreed increasing to 29/35 (83%). For overall satisfaction, 30/35 (86%) reported good or adequate and 19/35 (54%) agreed or strongly agreed with using the system again. CONCLUSIONS: These data suggest that a short introduction on how to use virtual systems may result in significant participation and use of virtual mentoring systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".