The Challenge with Clinical Radiology Electives: Student and Faculty Perspectives Identify Areas for Improvement
Bibliographic record
Abstract
PURPOSE: To determine medical students' and radiologists' attitude toward radiology electives at a distributed medical school and identify specific areas for improvement. METHODS: During a single academic year, both students and faculty preceptors were surveyed anonymously following a senior radiology elective. The survey was based on an established theoretical framework for studying the educational environment which takes into account domains: (1) goal orientation, (2) organization/regulation, and (3) relationships. Mann-Whitney tests were performed to determine if there was any difference between the overall satisfaction of students and preceptors, responses from the different elective sites and students' ratings of the domains. Statistical significance was set at P < .05. Thematic analysis was performed on the narrative comments to identify specific challenges. RESULTS: The response rate was 82.0% for students (95/116) and 19.5% (31/159) for radiologists. There was no difference in responses based on elective site. Overall, the elective was viewed positively by both groups however students rated their experience as significantly better than their preceptors (P = .0012). Students viewed the relationships domain more positively than both the other two (goal orientation, P = .0001; organization/regulation, P = .0038). Thematic analysis identified that the student challenges were lack of autonomy, structured teaching, and preceptor continuity and the preceptor challenges were ambiguous learning objectives/expectations and insufficient resources. CONCLUSIONS: The radiology elective challenges identified in this study provide educators with specific areas to target when updating radiology electives. A better elective experience may improve students' radiology knowledge and attitude towards the specialty as well as radiologists' interest in teaching.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".