Effectiveness of Near-Peer Versus Faculty Point-of-Care Ultrasound Instruction to Third-Year Medical Students
Bibliographic record
Abstract
Background: Incorporation of point-of-care ultrasound (POCUS) in undergraduate medical education (UME) is expanding; however, its effective implementation is impeded by a lack of trained faculty. Recruitment of near-peer (NP) instructors is a potential solution, but there are concerns surrounding NP teaching effectiveness compared to faculty instruction. While some institutions have assessed supplemental NP instruction, or NP-taught sessions with strict faculty supervision, few if any have compared effectiveness of NP POCUS instruction alone to faculty instruction through a multi-dimensional assessment. The aim of this study was to compare the effectiveness of near-peer (NP) instruction to faculty instruction at an undergraduate medical education clinical POCUS session for third-year medical students. Methods: This was a randomized controlled trial where third-year medical students were assigned to one of two groups for a 90-minute POCUS session: NP instruction or faculty instruction. A pre- and post-session multiple-choice test and a post-session objective structured clinical examination (OSCE) were administered to assess conceptual and hands-on clinical POCUS knowledge gained. Students’ perceptions of the instructors and session were evaluated using a Likert scale. Results: Seventy-three students (66% of the class) participated; 36 taught by faculty and 37 by NP instructors. Both groups showed a significant score increase from pre-test to post-test (p =0.002); however, there was no significant difference between groups in post-test (p=0.27) nor OSCE scores (p=0.20). Student perceptions of instructor competency were not statistically significant. Conclusions: NP instructors were as effective at teaching clinical POCUS to third-year medical students as faculty instructors at our institution.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".