Radiology Interest Groups: A Recipe for Success
Bibliographic record
Abstract
OBJECTIVES: Radiology Interest Groups (RIGs) are valuable for medical students and the radiology profession. The purpose of this study is to identify key components of a successful RIG and to discuss how to optimize available resources to increase student engagement in radiology. METHODS: Anonymous feedback forms (n = 478) completed by preclinical medical students attending 20 RIG events between September 2016 and May 2019 were analyzed. A five-point Likert-type scale was used to determine event effectiveness, and important themes reflecting student perspectives were identified using thematic analysis of freeform comments. RESULTS: Based on Likert feedback, 75% to 78% of students had a positive experience of RIG events and believed sessions were relevant to their studies. 31% to 42% of students believed these events increased their interest or insight into radiology and influenced their career choice. Four representative themes were identified by qualitative analysis of written feedback: engagement, professional development, mentorship, and suggestions for improvement. These themes provided insight into student perspectives of our RIG, and, along with experience from the RIG organizers, the authors present elements perceived to have contributed to these positive results. CONCLUSION: Thematic analysis of feedback reveals that students consider interactive events, contribution to professional development, and the opportunity for mentorship valuable elements of a RIG. From the perspective of the organizing committee, we embody these aspects through careful planning, innovative events, and consistent debriefing. In this way, our RIG promotes the future of the radiology profession and serves as a practical model for other similar organizations.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".