Medical Student POCUS Peer-to-Peer Teaching: Ready for Mainstream
Bibliographic record
Abstract
Background: Point of care ultrasound (POCUS) is changing the face of clinical practice and medical education. Worldwide consensus based on expert opinion has advocated for POCUS teaching in undergraduate medical school curricula. Significant barriers, including lack of available instructors and limited resources, prevents medical learners from acquiring core competencies at most institutions. Here, we describe a peer-to-peer learning POCUS workshop and advocate for the use of this type of training to meet the demands of POCUS learning. Methods: A two-day POCUS workshop was held in Toronto, Ontario with twenty-six medical student participants. The workshop was structured according to a graduated model of POCUS skill development, beginning with didactic teaching, then progressing to hands-on peer-to-peer teaching, and finishing with competency evaluation by POCUS experts. Participants completed pre-and post-workshop surveys regarding prior POCUS teaching and exposure, self-reported skill development, and feedback on the workshop itself. Results: Of the 20 respondents to the questionnaire, 70% had prior POCUS exposure, with 85% of these individuals having less than 5 hours of prior POCUS education. Eighty-five percent of students reported that the organization of the course allowed them to participate fully, and 95% of participants indicated that peer-to-peer learning was effective. Conclusion: These findings suggest that peer-to-peer POCUS teaching is an effective learning method to acquire and consolidate well-established POCUS competencies. This initiative is scalable and could be applied to all learners in various disciplines. As such, we recommend medical schools consider integration of peer-to-peer POCUS teaching into longitudinal clerkship training programs, and transition-to-residency courses.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.020 |
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".