Flipping the Classroom in Otolaryngology Residencies
Bibliographic record
Abstract
Objective To understand the use of the flipped classroom (FC) - learning core content prior to an academic session, with class time devoted to applying this content - in otolaryngology residency education. Methods An electronic survey of 107 otolaryngology program directors (PDs), including demographic details, the flipped classroom perception instrument (FCPI), and the otolaryngology programs' current use of FC. Results Forty-four (41%) PDs completed the FCPI. Seventy-one point one (71.1%) of respondents were male, 60% were 30-49 years, and the remainder were older. Sixty-two percent (62%) had fellowships associated with their program, 21.7% of programs used the FC model Very Often, 17.4% Somewhat Often, 28.3% Sometimes, 17.4% Somewhat Rarely, 8.7% Very Rarely, and 6.5% Never. Attitudes toward FC principles were positive with modes "strongly agree" for all, except for "online modules enhance learning" where the mode was "slightly agree" with significantly higher scores for PDs over age 50 than for those younger (4.17 vs. 3.63, p=0.033). There were no other significant differences comparing male vs. female PDs, younger vs. older PDs, smaller vs. larger programs, programs with or without fellowships, programs with 100% vs. <100% board exam pass rates, or programs in different geographical regions. The pre-class activity mean score was 4.34 (95% CI 4.12-4.56) and the in-class mean score was 4.18 (95% CI 3.99-4.37). There was no significant correlation between the likelihood of using a flipped classroom and attitude scores. Conclusion PDs value both the pre-class and interactive in-class principles of FCs but only 37.8% of programs use FC often, suggesting that practical approaches to implementation in this group could improve education in this population.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".