Peer-Led Point-of-Care Ultrasound; A Potential Ally to Rural Medicine
Bibliographic record
Abstract
Objectives Point of care ultrasound (POCUS) is increasingly used in rural settings where it’s portability,and imaging capabilities make it effective clinically. POCUS teaching has traditionally relied on faculty instruction, which is limited by the small number of certified faculty members. The UOttawa POCUS interest group deployed peer-teaching since 2018, which overcomes the instructor barrier by employing experienced medical students to train preclerkship students. This paper will evaluate the efficacy of the peer-led POCUS workshops as a learning format. Methods 3-hour POCUS workshops were held for Cardiac, MSK, Aorta, and eFAST scans from October 2018 to June 2019. Students with prior experience in POCUS were identified as peer-teachers, and were trained by an expert physician prior to the workshop. Peer-teachers taught a small group, with physician experts rotating through groups for technical support. Surveys were sent out to students who participated in the workshops assessing the following categories:utility, learning experience,workshop efficacy, tutor competence, and interest. Descriptive statistics and thematic analysis was reported for the quantitative and qualitative data, respectively. Results 45 participants completed the survey. The surveys showed positive support for the aforementioned categories, with the average score being greater than 4. From the thematic analysis, the four main strengths of the peer-led format are: Trainer competence, learner comfort, situational teaching, and opportunity to practice. Conclusion Peer-led workshops are an effective format for POCUS training in instructor-constrained settings. These workshops can be translated to rural settings in lieu of a formal POCUS training program.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".