A classification of the verbal methods currently used to teach endoscopy
Bibliographic record
Abstract
BACKGROUND: As endoscopy does not lend itself well to assisting or exposure by the teacher, most of the teaching is, by necessity, done verbally. METHODS: The verbal teaching occurring during 19 colonoscopies and 14 gastroscopies was recorded by dictaphone and later transcribed. The resultant 53-page transcript was then analyzed using the Grounded Theory method. Teaching was compared between learners with less than one month versus more than one month of training and between teaching of colonoscopy versus gastroscopy. RESULTS: The process of iterative review and repeated testing yielded 6 types of verbal teaching: demonstration by the teacher, motor instructions, broad tips/tricks/pointers, verbal feedback, questioning, and non-procedural information. Inter-rater agreement was excellent (Fleiss's kappa = 0.76) between resident (DM), the non-medical educator (MP), and the medical teacher (MM). Overall, there was less non-procedural teaching (6.7% vs 23.7%, p = 0.01) and a trend towards more teaching moments per case (13.2 vs 7.9, p = 0.07) in the first month of the rotation compared to the later months. A greater proportion of the teaching for colonoscopy involved demonstration (13.7% vs. 2.7%, p = 0.040) and tips/tricks/pointers (26.6% vs. 12.4%, p = 0.012) compared to gastroscopy. CONCLUSIONS: We describe a means of categorizing verbal teaching in endoscopy that is simple and shows strong inter-rater agreement that will serve as a starting point for further studies aiming to improve how endoscopy is taught.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".