Diagnostic Neuroradiology Subspecialty Training: 1 Versus 2 Years; the Canadian Perspective
Bibliographic record
Abstract
Background: Canada began a national reform of its post-graduate medical education training programs to a Competence By Design (CBD) model. Trends from accredited neuroradiology programs from the past 10 years were investigated to inform educators and stakeholders for this process. Methods: A 13-question electronic survey was sent to program directors of all 8 accredited neuroradiology training programs in Canada. Data was requested for each year on the 2008-2019 graduating classes. Questions pertained to program enrolment; program completion; post-training employment; and the sufficiency of 1-year training programs. Results: Response rate was 100%. Over the timeframe studied, the 2-year programs increased in size ( P = 0.007), while the 1-year programs remained steady ( P = 0.27). 12.2% of trainees enrolled in the 2-year program dropped out after 1 year, and were considered 1-year trainees thereafter. A higher proportion of 2-year trainees obtain positions within academic institutions (89.5 vs 67.2%, P = 0.0007), whereas a higher proportion of 1-year trainees obtain positions within non-academic institutions (29.3 vs 8.1%, P = 0.0007). A higher proportion of those with Canadian board certification in diagnostic radiology who completed a 2-year program obtained a position within a Canadian academic institution compared to non-certified 2-year trainees ( P < 0.001). 71.4% of program directors agreed that a 1-year program was sufficient for non-academic staff positions. Conclusion: The length of the training program has significant impact on employment in academic vs non-academic institutions. This information can be used to guide the upcoming CBD initiative for neuroradiology programs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".