Assessing the role of virtual reality training in Canadian Otolaryngology–Head & Neck Residency Programs: A national survey of program directors and residents
Bibliographic record
Abstract
BACKGROUND: Given mounting pressure of work hour restrictions, resource constraints, and variability of clinical exposure, Otolaryngology-Head & Neck Surgery (OHNS) residency training has shifted away from the apprenticeship model to embrace the Royal College of Physicians and Surgeons of Canada's "Competence by Design" initiative. As a result, appraising both current and potential educational adjuncts has become increasingly important. In this investigation, a national needs assessment survey was performed to identify strengths, weaknesses, and future opportunities of the current training landscape. METHODS: An online survey was distributed to all thirteen Canadian OHNS post-graduate administrators for completion by program directors and residents from February to October in 2016. Prior to distribution, the survey was vetted for face validity by a group of staff Otolaryngologists and questions were modified accordingly. Quantitative analysis was performed on SPSS (IBM Corp., Chicago) with non-parametric, two-tailed Mann-Whitney U testing performed on scaled questions. RESULTS: Of the 68 responses, 11 of 13 (84.6%) of program directors and 57 of 168 (33.9%) residents responded to the survey. All 13 programs currently utilize cadaveric laboratory dissections. Associated challenges were ranked as specimen availability, faculty participation, insufficient space, and resident time constraints. 30.8% of programs currently utilize some form of virtual reality simulator, which 90.9% of program directors felt would be a fair and effective platform for evaluation. CONCLUSION: A discrepancy exists between the favourable attitudes of both residents and program directors towards virtual reality simulation and its actual adoption. For successful adoption to occur, the existing barriers to unconventional training must be addressed and the tangible benefits for competency based training will need to be explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".