Acquisition and Long‐term Retention of Bedside Ultrasound Skills in First‐Year Medical Students
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to assess bedside ultrasound skill acquisition and retention in medical students after completion of the first year of a new undergraduate bedside ultrasound curriculum at McGill University. METHODS: Skill acquisition was assessed in first-year medical students (n = 195) on completion of their bedside ultrasound instruction. Instruction included 6 clinically based 60-minute practical teaching sessions evenly spaced throughout the academic year. Students' ability to meet course objectives was measured according to a 4-point Likert rating scale. Evaluations were performed by both instructors and the students themselves. Retention of skill acquisition was evaluated 8 months later on a year-end practical examination. RESULTS: The mean percentage ± SD of students assigned a rating of "strongly agree" or "agree" by instructors was 98% ± 0.4% for all 6 teaching sessions (strongly agree, 52% ± 3%; agree, 46% ± 3%). According to student self-evaluations, the mean percentage of students assigned a rating of strongly agree was significantly greater than the percentage assigned by instructors for all teaching sessions (86% ± 2% versus 52% ± 3%; P < .0005). Evaluation of skill retention on the year-end examination showed that 91% ± 2% of students were assigned a rating of strongly agree or agree for their ability to demonstrate skills learned 8 months previously. Ninety-five percent of students reported that bedside ultrasound improved their understanding of anatomy for all 6 teaching sessions (mean, 95% ± 0.01%). CONCLUSIONS: These results demonstrate that first-year medical students show acquisition and long-term retention of basic ultrasound skills on completion of newly implemented bedside ultrasound instruction.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".