Point-of-Care Ultrasound and Procedural Instruction in the Family Medicine Clerkship: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Point-of-care ultrasound (POCUS) education has become a mainstay in resident education in multiple specialties, including family medicine (FM), but literature regarding the use of POCUS during clinical medical student education is lacking. The purpose of this study was to investigate whether and how POCUS education is conducted in FM clerkships in the United States and Canada and how it compares to more traditional FM clinical procedural instruction. METHODS: As part of the 2020 Council of Academic Family Medicine's Educational Research Alliance survey of FM clerkship directors, we surveyed clerkship directors in the United States and Canada about whether and how POCUS education, as well as other procedural instruction in their institutions and FM clerkships, was conducted. We included questions regarding POCUS and other procedural use by preceptors and faculty. RESULTS: We found that 13.9% of clerkship directors reported structured POCUS education during clerkship, while 50.5% included other procedural training. The survey revealed that 65% of clerkship directors felt that POCUS was an important component of FM, but this was not a predictor of POCUS use in personal or preceptor practice nor of its inclusion in FM clerkship education. CONCLUSIONS: Structured POCUS education is a rare component of FM clerkship education; while more than half of clerkship directors felt that POCUS was important for FM, few used it personally or included it in clerkship education. As POCUS continues to be integrated into medical education in FM, the clerkship may represent an opportunity to expand POCUS exposure for students.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".