An evaluation of the COVID-19 pandemic impact on Canadian undergraduate dental students' perceived preparedness and actual productivity in clinical dentistry
Bibliographic record
Abstract
Objective: To investigate the ways in which the COVID-19 pandemic impacted the dental education, preparedness and productivity of Canadian dental students in two schools. Methods: Research data was collected via three primary methods. The first was through the Student Oriented Learning Online (SOLO) Survey. Survey responses from third and fourth year dental undergraduate students at the University of British Columbia and University of Toronto were compiled and analyzed. Secondly, clinical procedural productivity data was collected from the University of British Columbia Faculty of Dentistry for the dental student cohorts of 2016-2025 and analyzed. Lastly, data was collected from semi-structured interviews with dental students at both of the aforementioned universities. Analysis of the interview data revealed important themes. Results: From the SOLO survey data, it was found that UBC students showed higher scores for all six studied outcomes in comparison to UofT students. No significant differences were found among UBC respondents between Terms 1 and 2, but for UofT students two of the six outcomes were significantly decreased in Term 2 compared to Term 1. From the clinical procedural productivity data, it was found that there was a significant decrease in the number of procedures completed by the Classes of 2020 and 2021. Lastly, from the semi structured interviews, six overall themes were identified as to how the COVID-19 pandemic impacted these Canadian dental students. Conclusions: The two Canadian dental schools’ students felt that the COVID-19 pandemic had a significantly negative impact on their dental education and level of preparedness. The pandemic and related restrictions resulted in a decline of the number of clinical procedures completed by dental students. This study sheds light on various clinical preparation questions dental educators should consider.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".