Developing an Undergraduate Ultrasound Curriculum: A Needs Assessment
Bibliographic record
Abstract
Background The introduction of ultrasound into the undergraduate medical school curriculum is gaining momentum in North America. At present, many institutions are teaching ultrasound to undergraduate medical students using a traditional framework designed to instruct practicing clinicians, or have modeled the curriculum on other universities. This approach is not based on educational needs or supported by evidence. Methods Using a descriptive, cross-sectional survey of stakeholder groups, we assessed the perceived relevance of various ultrasound skills and the attitude towards implementing an undergraduate ultrasound curriculum at our university. Results One hundred and fifty survey respondents representing all major stakeholder groups participated. All medical students, 97% of residents and 82% of educators agreed that the introduction of an ultrasound curriculum would enhance medical students' understanding of anatomy and physiology. All clinical medical students and residents, 92% of preclinical medical students, and 82% of educators agreed that the curriculum should also include clinical applications of ultrasound. Participants also indicated their preferences for specific curriculum content based on their perceived needs. Conclusion An integrated undergraduate ultrasound curriculum composed of specific preclinical and clinical applications was deemed appropriate for our university following a comprehensive needs assessment. Other universities planning such curricula should consider employing a needs assessment to provide direction for curriculum need and content.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".