Imaging the Future: Student Reflections on an Innovative Undergraduate Radiology Course
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Radiology plays a critical role in modern healthcare, yet it is often underrepresented in undergraduate education. To address this gap, the University of British Columbia (UBC) introduced RADS 301: exploring imaging in the 21st century, the first undergraduate radiology course of its kind in Canada. This study aims to examine the demographic backgrounds, motivations, and course evaluations of students enrolled in RADS 301. METHODS: An anonymous end-of-course survey was administered to students enrolled in RADS 301 during the 2024 academic term. The survey included demographic questions, multiple-choice and Likert-scale items evaluating various aspects of the course, and an open-ended feedback section. Descriptive statistics were used to analyze categorical and ordinal data, while qualitative responses were thematically reviewed. RESULTS: Out of 187 enrolled students, 178 (95%) completed the survey. Students represented a diverse range of academic disciplines, with just over half (55%) enrolled in Health and Life Science majors and the remainder from a variety of other fields, including humanities, business, and arts. Primary motivations for taking the course included general interest in medical topics (78%) and career exploration in healthcare (52%). Course evaluations demonstrated high satisfaction across all domains, with mean ratings ranging from 4.51 (SD = 0.60) to 4.70 (SD = 0.51) on a five-point scale. The overall course rating was 4.76 (SD=0.45). Qualitative responses emphasized the engaging content, diverse instructors, impact on career clarity, and improvements in health literacy. CONCLUSION: RADS 301 is a promising model for early exposure to radiology and interdisciplinary healthcare education at the undergraduate level. The course was well-received by students from a wide array of backgrounds and appears to enhance interest, knowledge, and confidence in radiology. Findings support the expansion of similar undergraduate courses to foster informed career exploration and promote health literacy.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".