Radiology Exposure in the Undergraduate Curriculum: A Medical Student Perspective on Quality and Opportunities for Positive Change
Bibliographic record
Abstract
PURPOSE: This article is a continuation of a qualitative study designed to explore how radiology exposures can impact medical student opinions and perceptions of radiology and radiologists. We focused on: 1) conducting a radiology exposure inventory from the perspective of the medical student; 2) student evaluation of the quality of the radiology exposures and suggestions for positive change; and 3) development of a framework to address the needs of medical students as it relates to radiology education in the undergraduate medical curriculum. METHODS: Research methodology and design for this qualitative study were described in detail in a previous article by Visscher et al [1]. RESULTS: Participants included 28 medical students; 18 were in medical school years 1 and 2 (preclerkship), and 10 were in years 3 and 4 (clerkship). Specific to the focus of this article, the data revealed 3 major findings: 1) multiple exposures to radiology exist, and they are received and valued differently depending on the medical student's stage of professional development; 2) medical students value radiology education and want their radiology exposure to be comprehensive and high quality; 3) Medical students have constructive suggestions for improving the quality of both formal and informal radiology exposures. CONCLUSIONS: Performing a radiology exposure inventory from a medical student perspective is a useful way to explore how students receive and value radiology instruction. Medical students want a more comprehensive radiology education that can be summarized using the 5 C's of Radiology Education framework. The 5 C's (curriculum, coaching, collaborating, career and commitment) reflect medical students' desires to learn content that will support them in clinical practice, be supported in their professional development, and have the necessary information to make informed career decisions.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".