Stressors and mental health impacts of COVID‐19 in dental students: A scoping review
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has led to mental health hazards to healthcare providers in general and to undergraduate dental students. The present study undertakes a scoping review to identify and discuss the mental health issues and stressors impacting dental students globally during the COVID-19 pandemic. METHODS: A systematic search following the Joanna Briggs Institute's framework for scoping reviews was conducted on Embase, MEDLINE by Ovid, Web of Science, PsycInfo, CINAHL, and ERIC to identify peer reviewed publications published globally since the onset of the COVID-19 pandemic in March 2020 until June 22, 2021. Manual search was also performed on Google scholar. Eligible studies were selected based on the predetermined set of criteria and focused specifically on mental health issues experienced by undergraduate dental students in the context of COVID-19. RESULTS: Fifty-five publications were retained. Fifty-one reported stress, anxiety, or depression experienced by undergraduate dental students during the pandemic. Twenty-five studies employed validated psychometric instruments to explore mental health status among the same students. A total of 28 publications reported that the predominant concern for students was fear of infection, especially during patient interaction. Adapting to virtual learning, clinical skill deficit due to lack of/limited practical training and social isolation were other majorly reported stressors. CONCLUSIONS: Many mental health issues and stressors related to the COVID-19 pandemic have been identified. Mitigation of COVID-19-related stressors on dental students' mental health should be a priority across all dental academic institutions. Continued efforts should be made to create a supportive learning environment.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".