Supplementary Educational Models in Canadian Neurosurgery Residency Programs
Bibliographic record
Abstract
BACKGROUND: The proposed implementation of work hour restrictions has presented a significant challenge of maintaining the quality of resident education and ensuring adequate hands-on experience that is essential for novice surgeons. To maintain the level of resident surgical competency, revision of the apprentice model of surgical education to include supplementary educational methods, such as laboratory and virtual reality (VR) simulations, have become frequent topics of discussion. We aimed to better understand the role of supplementary educational methods in Canadian neurosurgery residency training. METHODS: An online survey was sent to program directors of all 14 Canadian neurosurgical residency programs and active resident members of the Canadian Neurosurgical Society (N=85). We asked 16 questions focusing on topics of surgeon perception, current implementation and barriers to supplementary educational models. RESULTS: Of the 99 surveys sent, 8 out of 14 (57%) program directors and 37 out of 85 (44%) residents completed the survey. Of the 14 neurosurgery residency programs across Canada, 7 reported utilizing laboratory-based teaching within their educational plan, while only 3 programs reported using VR simulation as a supplementary teaching method. The biggest barriers to implementing supplementary educational methods were resident availability, lack of resources, and cost. CONCLUSIONS: Work-hour restrictions threaten to compromise the traditional apprentice model of surgical training. The potential value of supplementary educational methods for surgical education is evident, as reported by both program directors and residents across Canada. However, availability and utilization of laboratory and VR simulations are limited by numerous factors such as time constrains and lack of resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".