COVID-19 and the transition to virtual teaching sessions in an orthopaedic surgery training program: a survey of resident perspectives
Bibliographic record
Abstract
Background COVID-19 has had a tremendous impact on medical education. Due to concerns of the virus spreading through gatherings of health professionals, in-person conferences and rounds were largely cancelled. The purpose of this study is the evaluate the implementation of an online educational curriculum by a major Canadian orthopaedic surgery residency program in response to COVID-19. Methods A survey was distributed to residents of a major Canadian orthopaedic surgery residency program from July 10th to October 24th, 2020. The survey aimed to assess residents’ response to this change and to examine the effect that the transition has had on their participation, engagement, and overall educational experience. Results Altogether, 25 of 28 (89%) residents responded. Respondents generally felt the quality of education was superior (72%), their level of engagement improved (64%), and they were able to acquire more knowledge (68%) with the virtual format. Furthermore, 88% felt there was a greater diversity of topics, and 96% felt there was an increased variety of presenters. Overall, 76% of respondents felt that virtual seminars better met their personal learning objectives. Advantages reported were increased accessibility, greater convenience, and a wider breadth of teaching faculty. Disadvantages included that the virtual sessions felt less personal and lacked dynamic feedback to the presenter. Conclusions Results of this survey reveal generally positive attitudes of orthopaedic surgery residents about the transition to virtual learning in the setting of an ongoing pandemic. This early evaluation and feedback provides valuable guidance on how to grow this novel curriculum and bring the frontier of virtual teaching to orthopaedic education long-term.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".