Lessons Learned From the Effects of COVID-19 on the Training and Education Workflow of Radiology Residents—A Time for Reflection: Perspectives of Residency Program Directors and Residents in Canada
Bibliographic record
Abstract
PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to widespread changes in all health care settings including academic radiology departments. The purpose of this survey-based study was to investigate the impact of COVID-19 on radiology resident training and education workflow in Canada in terms of the nature, scale, and heterogeneity of the changes, preparedness and adaptation, and perceptions of the present and future of radiology training. METHODS: A 30-question web-based survey was sent to 17 radiology residency program directors across Canada. A separate 32-question survey was sent to 460 residents currently enrolled in a radiology residency in Canada. These surveys were open for 3 weeks. RESULTS: We received responses from 16 program directors and 80 residents (response rates 94.1% and 17.4%, respectively). Most respondents agreed that objectives were being met for knowledge and interpretation but less so for case volumes and technical skills. Less time was allotted for on-site activities (eg, readouts) with more time for off-site activities (eg, videoconferencing). Daytime rotations were at least partly cancelled. Most respondents felt these changes were met with enthusiasm by both faculty and residents. However, there were perceived challenges including lack of training on virtual platforms for delivery of teaching and decreased staff-resident interaction, with short- and long-term anxiety reported. CONCLUSIONS: The coronavirus disease 2019 has dramatically changed radiology resident training in Canada, with increased virtual learning at the expense of cancelled rotations and the resultant reduction in case volumes and staff-resident interaction. Although adopted with enthusiasm, these changes present substantial challenges and anxiety regarding the future of radiology resident education.
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How this classification was reachedexpand
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".