Peer teachers as ultrasound instructors? – a systematic literature review of peer teaching concepts in undergraduate ultrasound education
Bibliographic record
Abstract
BACKGROUND: As ultrasound is one of the most utilized imaging procedures in clinical practice in Germany, its integration into undergraduate medical education is imperative. Thereby, the limited availability of qualified instructors is a major challenge. Peer tutors, who are trained to instruct their peers collaboratively, could resolve staff constraints. This systematic review explores the literature on peer teaching in undergraduate ultrasound education, aiming to provide an overview of methodologies, outcomes, and peer teacher training concepts. METHODS: Following the PRISMA guidelines, a systematic literature review was conducted on the subject of peer teaching in undergraduate ultrasound education. Using PubMed and Google Scholar as databases, studies in English or German involving training concepts for peer teachers in undergraduate ultrasound education, published up to November 21, 2023, were included. Data extraction of original studies followed the PICOS schema with special respect to didactic concepts of peer tutor training programs and the effectiveness of peer teachers compared to faculty instructors. A modified version of the Newcastle-Ottawa Scale (NOS) was used to assess the quality of included studies. RESULTS: Finally, the search resulted in 20 relevant original studies, including 16 studies exploring peer teacher training concepts. Predominantly, peer teachers studied in their 4th year of medical school and on average one year further compared to their students. Peer teacher training was integrated into curricula by course-based concepts (93.8%) and internships (50.0%). Didactic modalities varied, encompassing laboratory rotations including the scanning of patients, the scanning of fellow students, lectures, and didactic training. The median training duration was about ten days. Of six comparative studies, five found peer-assisted learning to be comparably effective and one even better than faculty-led courses. CONCLUSION: Despite the growing amount of literature underlining the effectiveness and wide application of peer teaching in ultrasound education, training concepts stay heterogenous without a standardized system for training and qualifying peer teachers. Developing comprehensive guidelines for peer tutor education could increase acceptance and recognition of peer-assisted learning and ensure minimum training standards.
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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 | 0.024 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".