Depression and Anxiety Among Dentists: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background and Aims Many studies investigated the prevalence and severity of depression and anxiety among dentists. This systematic review aimed to determine: (i) the prevalence and severity of depression and anxiety symptoms, (ii) the prevalence rates of depression and anxiety before and during the COVID‐19 pandemic, and (iii) gender difference in prevalence of depression and anxiety among dentists. Methods Eligible articles on depression and anxiety in dentists were systematically searched for in PubMed and Scopus databases from September 2023 to October 2023 according to the Preferred Reporting Items for Systematic Review and Meta‐Analysis protocol. We assessed the methodological quality of the studies using the Newcastle–Ottawa Quality Assessment checklist adapted for cross‐sectional studies. Statistical heterogeneity across the studies was evaluated using Cochran's Q test and I 2 statistic. The prevalence rates of depression and anxiety were calculated using the random‐effect model with the Restricted Maximum‐Likelihood estimator. Of 3762 searched articles, 33 articles were analyzed. Results The prevalence rates of depression and anxiety symptoms among dentists were 42% and 44%, respectively. The prevalence rates of mild, moderate, and severe or extremely severe depression were 20%, 18%, and 8%, respectively. For mild, moderate, and severe or extremely severe anxiety, the respective prevalence rates were 21%, 18%, and 11%. We did not find evidence to suggest differences in depression or anxiety prevalence rates between the periods before and during COVID‐19. In comparison with men, women showed approximately 27% higher risk of experiencing depression and 24% higher risk of experiencing anxiety. Conclusion Equally high levels of depression and anxiety in dentists were found both before and during the COVID‐19 pandemic, with a significant percentage of moderate to severe depression and anxiety. Female dentists reported a higher prevalence of depression and anxiety symptoms than their male colleagues.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".