Canadian Medical Education Journal Major Contribution/Research Article Exploring Surgeons ' Perceptions of the Role of Simulation in Surgical Education: A Needs Assessment
Bibliographic record
Abstract
Background: The last two decades have seen the adoption of simulation-based surgical education in various disciplines. The current study’s goal was to perform a needs assessment using the results to inform future curricular planning and needs of surgeons and learners. Methods: A survey was distributed to 26 surgeon educators and interviews were conducted with 8 of these surgeons. Analysis of survey results included reliability and descriptive statistics. Interviews were analyzed for thematic content with a constant comparison technique, developing coding and categorization of themes. Results: The survey response rate was 81%. The inter-item reliability, according to Cronbach’s alpha was 0.81 with strongest agreement for statements related to learning new skills, training new residents and the positive impact on patient safety and learning. There was less strong agreement for maintenance of skills, improving team functioning and reducing teaching in the operating room. Interview results confirmed those themes from the survey and highlighted inconsistencies for identified perceived barriers and a focus on acquisition of skills only. Interview responses specified concerns with integrating simulation into existing curricula and the need for more evaluation as a
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.004 |
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".