An Assessment of Various Challenges Perceived by Dental Students amidst the COVID-19 Pandemic: A Digital Questionnaire Study
Bibliographic record
Abstract
The objective of our study was to evaluate dental students’ perception of the challenges faced during the COVID-19 pandemic related to their clinical work, education, performance, online examinations, psychological health, and teamwork. A validated online questionnaire consisting of closed ended questions was sent to all the undergraduate dental students at our institute. Data were collected and analyzed statistically using a chi-square test to compare responses of male with female and junior with senior students. A total of 317 undergraduate dental students (N = 317) participated in this cross-sectional study. The most common challenges perceived by the participants were related to their exam duration (77.3%), patient availability (66.9%), difficulty in understanding online lectures (58.4%), and a fear of losing grades (57.4%). Participants reported that the COVID-19 pandemic affected their performance in the courses (75.4%), teamwork (72.2%), educational aspects (67.5%), and psychological health (51.1%). A significantly greater proportion of female students reported the adverse effects of COVID-19 on their psychological health than male students (p = 0.031). Senior students perceived the negative impact of COVID-19 on teamwork significantly more than the junior students (p = 0.004). The majority of students reported challenges during the COVID-19 pandemic. Female students and senior students perceived more challenges than their counterparts. Future studies from other institutes of this region are recommended to establish a clearer picture of COVID-19 related challenges faced by dental students.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".