Online residency training during the COVID-19 pandemic: A national survey of otolaryngology head and neck surgery program directors
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has deeply impacted healthcare and education systems, including resident education. The impact of the pandemic on the different types of pedagogical activities, and the displacement of pedagogical activities to online modalities have not yet been quantified. We sought to evaluate the impact of the COVID-19 pandemic on formal pedagogic components of otorhinolaryngology-head and neck surgery (ORL-HNS) residency, the switch to distance learning and program director's perceptions of the future of teaching and learning. METHODS: A nationwide online survey was conducted on Canadian ORL-HNS program directors. The use of standard didactic activities in-person and online, before and during the pandemic was rated with Likert scales. Perceptions of the pandemic were described with open-ended questions. RESULTS: A total of 11 of the 13 program directors contacted responded. The analysis were conducted using nonparametric statistics. There was a significant drop in overall didactic activities during the pandemic, regardless of the teaching format (3.5 ± 0.2 to 3.1 ± 0.3, p < 0.05). The most affected activities were simulation and in-house lectures. Online activities increased dramatically (0.5 ± 0.2 to 5.0 ± 0.5, p < 0.001), including attendance to lectures made by other programs (0.5 ± 0.3 to 4.0 ± 0.8, p < 0.05). Respondents stated their intention to maintain the hybrid online and in-person teaching model. CONCLUSIONS: These findings suggest that hybrid online and in-person teaching is likely to persist in the post-pandemic setting. A balanced residency curriculum requires diversity in academic activities. The pandemic can have positive consequences if higher education institutions work to better support distance teaching and learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".