Improving learning and confidence through small group, structured otoscopy teaching: A prospective interventional study
Bibliographic record
Abstract
Background Otologic diseases are common and associated with significant health care costs. While accurate diagnosis relies on physical exam, existing studies have highlighted a lack of comfort among trainees with regards to otoscopy. As such, dedicated otoscopy teaching time was incorporated into the undergraduate medical curriculum in the form of a small group teaching session. In this study, we aimed to examine the effect of a small-group, structured teaching session on medical students’ confidence with and learning of otoscopic examination. Methods Using a prospective study design, an otolaryngologist delivered an one-hour, small group workshop to medical learners. The workshop included introduction and demonstration of otoscopy and pneumatic otoscopy followed by practice with peer feedback. A survey exploring students’ confidence with otoscopy and recall of anatomical landmarks was distributed before(T1), immediately after(T2), and 1 month following the session(T3). Results One hundred and twenty five learners participated from February 2016 to February 2017. Forty nine participants with complete data over T1-T3 demonstrated significant improvement over time in confidence ( Wilk's lambda = .09, F(2,48) = 253.31 p < .001, η 2 = .91) and learning ( Wilk's lambda = 0.34, F(2,47) = 24.87 p < .001, η 2 = .66). Conclusions A small-group, structured teaching session had positive effects on students’ confidence with otoscopy and identification of otologic landmarks. Dedicated otoscopy teaching sessions may be a beneficial addition to the undergraduate medical curriculum.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".