Bibliographic record
Abstract
Objective: At Texas College of Osteopathic Medicine (TCOM), point of care ultrasound (POCUS) is taught to medical students in conjunction with trained medical student teaching assistants (TAs). The purpose of our study is to evaluate the effectiveness of near peer teaching in the setting of ultrasound education. We hypothesized that this would be the preferred learning technique among TCOM students and TAs. Methods: To evaluate our hypotheses about the value of near peer instruction, we created two comprehensive surveys for students to share their experiences with the ultrasound program. One survey was for general students and the other survey was for students designated as TAs. The surveys were sent via email to second and third-year medical students. Results: General Student Population Survey Results: Of the 63 students who took the survey, 90.4% agreed that ultrasound is an integral part of medical education, 79.4% of students either agreed or strongly agreed that ultrasound improves their understanding of systems-based course material, 53.9% of students prefer near peer techniques over other teaching methods, while only 38.7% of students would prefer faculty-led sessions. 73% of students agreed that their ultrasound skills have improved with peer-led sessions, 71.4% of students agreed that peer-led sessions have made them want to pursue additional ultrasound training, and 96.8% of students report that they are very likely or somewhat likely to use POCUS in their future practice. Ultrasound Teaching Assistant Survey Results: Nineteen TAs responded to the survey, of which 78.9% assisted with more than 4 teaching sessions, 84.2% attended more than 4 TA training sessions, 94.7% reported spending additional time practicing ultrasound outside of TA activities each week, 100% agreed or strongly agreed that being an ultrasound TA has helped their medical education, and 78.9% either agreed or strongly agreed that they feel competent in their ultrasound skills. Among TAs, 78.9% preferred near peer techniques over other teaching methods, 100% agreed or strongly agreed that being a TA has helped develop their ultrasound skills, and 100% were likely or very likely to use POCUS in their future practice. Conclusions: Based on the results of our surveys, we were able to conclude that near peer teaching is the preferred learning method among students at our institution, and TCOM students found ultrasound to be a beneficial adjunct to systems courses in medical school education.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".