Canadian Radiology Medical Student Interest Groups: What They Are and How We Can Help Them Improve
Bibliographic record
Abstract
BACKGROUND: Radiology interest groups (RIGs) can serve as a means of increasing exposure of the radiology specialty early in the medical curriculum while also increasing educational opportunities. However, the organizational structure and various functions of individual RIGs in Canada are not well-documented. We performed a survey of all active RIGs in Canada for the purpose of better understanding their structure, function, and opportunities for improvement. METHODS: A 21-question survey was sent to current or recent former medical student leaders of all active RIGs in Canada during the 2016-2017 academic year. RESULTS: Radiology interest groups were identified in 88% (15/17) of Canadian medical schools. We received a 100% (15/15) response rate. Events held by RIGs consist mostly of lunch and learns (67%, 10/15), career panels (53%, 8/15), networking events (40%, 6/15), and curriculum-related events (40%, 6/15). General mentorship (93%, 13/14), shadowing opportunities (86%, 12/14), and research mentorship (63%, 8/14) were most often cited in their top 3 choices for opportunities for improvement. Sixty-six percent indicated that if a radiology society were to host a page for their interest group, they would be interested in posting content and/or links. CONCLUSIONS: Canadian RIGs offer increased early awareness and education about radiology in the medical curriculum. Radiology departments can facilitate improvement in Canadian RIGs through targeted institutional mentorship, research opportunities, and shadowing programs for their members.
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 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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".