Impact of COVID-19 on Canadian Radiology Residency Training Programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The novel coronavirus disease (COVID-19) pandemic has swept the globe, with a domino effect on medical education and training. In this study, we surveyed Canadian radiology residents to understand the impact of the pandemic on their residency training, strategies utilized by the residency programs in mitigating those impacts, and factors important to residents in the selection of educational resources on COVID-19. METHODS: A 10-item questionnaire was distributed to 460 resident members of the Canadian Association of Radiologists. The survey was open for 2 weeks, with a reminder sent at half-way mark. RESULTS: We received 96 responses (response rate: 20.9%). The 4 highest affected domains of training were daytime case volumes (92.4%), daytime schedules (87.4%), internal and external assessments (86.5%), and vacation/travel (83.3%). Virtual teaching rounds (91.7%), change in schedules to allow staying home (78.1%), and virtual/phone readouts (72.9%) were the most utilized strategies by the Canadian radiology residency programs. Overall stress of exposure to the disease was moderate to low (86.5%). A minority of the residents were redeployed (6.2%), although most (68.8%) were on standby for redeployment. Residents preferred published society guidelines (92.3%), review papers (79.3%), video lectures (79.3%), and web tools (76.9%) for learning about COVID-19 imaging manifestations. CONCLUSION: The COVID-19 pandemic has had a significant impact on various domains of the Canadian radiology residency programs, which has been mitigated by several strategies employed by the training programs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it