Trends in Point of Care Ultrasound Familiarity Among Undergraduate Medical Clerkship Educators
Bibliographic record
Abstract
OBJECTIVES: Despite growing use of point of care ultrasound (POCUS), there remains a paucity of data about familiarity with POCUS among educators who dictate curricular content in undergraduate medical education. This paper aims to longitudinally characterize the level of comfort and frequency of POCUS use among faculty involved in undergraduate clerkship education. METHODS: A web-based cross-sectional survey assessing comfort, frequency of use, and awareness of indications for POCUS among faculty involved in Internal Medicine, Family Medicine, and Surgery undergraduate clerkship education in a single urban academic medical center in 2016 and again in 2022. RESULTS: A total of 45 responses from 2016 and 30 responses from 2022 are included. The percentage of faculty "not comfortable" with performing POCUS decreased from 78% to 46%, although the overall change in comfort was not statistically significant. Comfort interpreting POCUS images, frequency of POCUS use, and familiarity with the clinical applications of POCUS all improved. Faculty identified multiple barriers to more frequent POCUS use. CONCLUSIONS: Over a six-year period at one urban, academic medical center, comfort with POCUS and frequency of use have increased slightly but remain low among core faculty responsible for clerkship education. There are still large gaps in knowledge and very few faculty regularly use POCUS, which can be attributed to multiple different barriers.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".