The Status of Canadian Radiology Mentorship Programs, Where We Stand and Where to Improve
Bibliographic record
Abstract
Background: The importance of mentorship in medicine is well-established. Access to mentors is pivotal in enhancing career opportunities and networking, increasing research productivity, and overall wellness and resilience at all career stages. Our study aims to assess the current status of radiology mentorship programs for Canadian medical students and radiology residents. Methods: We distributed an anonymous survey to Canadian radiology program directors in December 2022. The questions pertained to the existing mentorship programs’ specific goals, structure, and success. Our null hypothesis was that medical students and residents have similar mentorship opportunities. Results: We have received 12 responses (a response rate of 12/16 = 75%), 9 of which had formal mentorship programs and 3 (25%) did not. Comparing the mentorship program for medical students and residents yielded a P -value = .11 > .05. This result does not reject our null hypothesis, indicating there is no significant difference between these 2 groups. Using qualitative analysis, we categorized the responses into 4 main themes: mentorship programs’ goals, structures, evaluation methods, and their results. Conclusion: Although our result did not reach statistical significance ( P -value = .11 > .05), the observed trend shows that one third of Canadian medical schools do not offer a radiology mentorship program for medical students, highlighting a potentially significant opportunity for improvement. Qualitative analysis shows that despite various methods for assigning mentees to mentors, developing formalized yet flexible mentorship models that allow students and residents to self-select their mentors might be more beneficial than randomly assigning mentors to them.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".