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Record W4395046311 · doi:10.24908/pocus.v9i1.16678

Trends in Point of Care Ultrasound Familiarity Among Undergraduate Medical Clerkship Educators

2024· article· en· W4395046311 on OpenAlexvenueno aff
Nilan Schnure, Mahmoud Mohamed Elfadil, Wilma Chan, Cameron Baston, Frances S. Shofer, Nova L. Panebianco

Bibliographic record

VenuePOCUS Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPoint of care ultrasoundMedicineMedical educationUndergraduate educationFamily medicineUltrasoundRadiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.364
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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