Flipping the Passive Radiology Elective by Including Active Learning
Bibliographic record
Abstract
OBJECTIVES: Exposure to radiology in undergraduate medical education is often restricted by other curriculum demands. Designing an effective radiology elective for medical students who choose to supplement their education can be challenging as it is often a passive observership-style elective. In this study, we examined the impact of incorporating an online learning platform and electronic book into radiology electives to stimulate active learning. MATERIALS AND METHODS: We enrolled 23 students who pursued a 2-week diagnostic radiology elective at our institution. Their radiology knowledge prior to the elective was assessed using 2 pretests. Students had opportunities to work with radiologists to review clinical imaging, attend academic rounds, and learn from the online learning resources. Their knowledge after the elective was assessed by readministering the 2 tests as "posttests." Students also ranked their perception of the elective experience and educational resources on a Likert scale from 1 to 5. RESULTS: = .001). Students also had favorable perceptions of the radiology elective experience and rated the electronic book (median score: 5 of 5) and online learning platform (4.5 of 5) as valuable educational resources. CONCLUSION: The implementation of an electronic book and online learning platform improved knowledge in radiology and resulted in positive student perceptions of the elective experience. This supports the use of online resources to facilitate independent self-learning for future radiology electives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".