Implementation of a “Flipped Classroom” for Neurosurgery Resident Education
Bibliographic record
Abstract
INTRODUCTION: Engaging residents across a multiyear training spectrum is challenging given the heterogeneity of experience and limited time available for educational activities. A "flipped classroom" model, in which residents prepare ahead of time for mentored topic discussions, has potential advantages. METHODS: We implemented a curriculum consisting of topics distributed across the specialty. Weekly, each resident was randomly assigned to research a specific aspect of an assigned topic appropriate to his or her level of experience: junior residents about what characterizes each clinical entity, midlevel residents about when to intervene, and chief residents about how to administer treatment. Residents completed an anonymous survey 6 months after implementation. Board examination performance was assessed before and after implementation. RESULTS: A total of 12 residents participated in the program. Weekly, 1.75±0.40 hours were spent in preparation, with senior residents reporting less time than junior residents. All residents indicated that the accumulation of experience across 7 years of residency was a major advantage of this program, and all preferred it to lectures. Performance on the board examination significantly increased after implementation (from 316±36 to 468±45, p<0.05). CONCLUSIONS: The flipped classroom is a viable approach to resident education and is associated with increased engagement and improved performance using validated knowledge-assessment tools.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".