A Comparison of Dental Education Between University of Toronto and Zhejiang University During COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Background: To compare the contingency modifications to the dental education between the University of Toronto Faculty of Dentistry and the Zhejiang University School of Stomatology during COVID-19 pandemic, and to share experiences in keeping the dental academic continuity, resuming practicing activities and preparing the faculty and students for a new normality. Three approaches were adopted to collect information and data: online interviews and email-contact with the instructors and the deans, a small-scale online survey of dental students, and official online announcements of various authorities. Results: The two universities shared similarity in changing trends, while differed in details. The delivery of lectures, seminars and exams was transitioned from in-person mode to online mode and has proceeded effectively and efficiently. The pre-clinical lab training and clinical rotation were the most retarded parts and will not be resumed until the settle-down of the pandemic. Research activities have been kept on at the best possible level. Since the Zhejiang University reopened the campus in May 2020, clinical activities and research works were in recovery with a cautiously-planned and gradual phased approach. Conclusion: Both universities have been trying their best to meet the academic needs of students while protect their health, and to keep alert to the real time epidemic situation in preparation for resumption. Dental institutions could take the COVID-19 pandemic as an opportunity to armor dental students with infection control measures prior to their reengagement into clinical practice. There is a need of a new normality for global dental education that spans time and space.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".