COVID-19’s Psychosocial Impact on American and Canadian Dental Students
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has significantly affected dental education, contributing to adverse psychological outcomes, especially among dental students. In this cross-sectional study, the psychosocial state of American and Canadian dental students was explored, with special emphasis on affective, behavioral, and cognitive well-being, during and after the initial lockdown. Methods: Dental students were invited to participate in an online survey. The questions evaluated the pandemic’s effect on affective, behavioral, and cognitive responses, and learning experiences. Results: A total of 287 dental students completed the online survey. Sadness and anticipation were the strongest emotions experienced during and following the lockdown. Student worries were classified into external and internal stressors. Females demonstrated higher fear, students from larger dental schools held higher emotions of anticipation and disgust. American students held higher anticipation scores both during and following the lockdown. Canadian, third and fourth years, and students from small dental schools were more aware of which authority to contact if their patient, who presented for care, was suspected of COVID-19 infection which emphasizes the need for a preparedness protocol. Conclusions: More studies that explore the broader scope of psychological aspects are necessary. Monitoring and developing treatment strategies for emergent mental conditions among dental students is extremely important in pandemic crisis management. The study highlights the need for development of standardized protocols and pandemic related health education topics as well as psychological interventions to better prepare dental students globally during the time of crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".