Point-of-Care Ultrasound in Family Medicine Residencies 5-Year Update: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2014, family medicine residency programs began to integrate point-of-care ultrasound (POCUS) into training, although very few had an established POCUS curriculum. This study aimed to evaluate the resources, barriers, and scope of POCUS training in family medicine residencies 5 years after its inception. METHODS: Questions regarding current training and use of POCUS were included in the 2019 Council of Academic Family Medicine Educational Research Alliance (CERA) survey of family medicine residency program directors, and results compared to similar questions on the 2014 CERA survey. RESULTS: POCUS is becoming a core component of family medicine training programs, with 53% of program directors reporting establishing or an established core curriculum. Only 11% of program directors have no current plans to add POCUS training to their program, compared to 41% in 2014. Despite this increase in training, the reported clinical use of POCUS remains uncommon. Only 27% of programs use six of the eight surveyed POCUS modalities more than once per year. The top three barriers to including POCUS in residency training in 2019 have not changed since 2014, and are (1) a lack of trained faculty, (2) limited access to equipment, and (3) discomfort with interpreting images without radiologist review. CONCLUSIONS: Training in POCUS has increased in family medicine residencies over the last 5 years, although practical use of this technology in the clinical setting may be lagging behind. Further research should explore how POCUS can improve outcomes and reduce costs in the primary care setting to better inform training for this technology.
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 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".