First year internal medicine residents’ self-report point-of-care ultrasound knowledge and skills: what (Little) difference three years make
Bibliographic record
Abstract
BACKGROUND: With increasing availability of point-of-care ultrasound (POCUS) education in medical schools, it is unclear whether or not learning needs of junior medical residents have evolved over time. METHODS: We invited all postgraduate year (PGY)-1 residents at three Canadian internal medicine residency training programs in 2019 to complete a survey previously completed by 47 Canadian Internal Medicine PGY-1 s in 2016. Using a five-point Likert scale, participants rated perceived applicability of POCUS to the practice of internal medicine and self-reported skills in 15 diagnostic POCUS applications and 9 procedures. RESULTS: Of the 97 invited residents, 58 (60 %) completed the survey in 2019. Participants reported high applicability but low skills across all POCUS applications and procedures. The 2019 cohort reported higher skills in assessing pulmonary B lines than the 2016 cohort (2.3 ± SD 1.0 vs. 1.5 ± SD 0.7, adjusted p-value = 0.01). No other differences were noted. CONCLUSIONS: POCUS educational needs continue to be high in Canadian internal medicine learners. The results of this needs assessment study support ongoing inclusion of basic POCUS elements in the current internal medicine residency curriculum.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".