Opportunities, Challenges, and Curricular Insights Identified by Graduate Orthodontic Educators During the 2020/21/22 Academic Years at the University of Toronto
Bibliographic record
Abstract
This study investigated the opportunities and challenges associated with pandemic-related changes to didactic and clinical teaching. Seven instructors that deliver didactic, and/or clinical components of the graduate orthodontics program at the University of Toronto were interviewed virtually on their teaching experiences during the 2020/2021/22 (Fall term) academic years. The data were analyzed using the thematic analysis technique described by Braun and Clarke (2006). The key curricular insights, and the opportunities and challenges of the modified didactic and clinical teaching were identified. Educators’ preferred mode of didactic teaching varied, and preferences were course-specific. Educators preferred to return to the pre-pandemic clinical curriculum. There was enthusiasm towards adopting a hybrid model and implementing online and digital tools in both didactic and clinical curricula. Educators indicated the need for institutional support (technological/ infrastructural/human resources, incentives, and training) to promote adoption of online didactic teaching, and better preparedness for the future.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".