Depression and depressive symptoms in physicians prior to the COVID-19 pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Mental health disorders, such as depression, can significantly impact a physician's well-being and the quality of care they provide. We conducted a systematic review and meta-analysis to identify risk factors and to estimate the prevalence of depression and depressive symptoms in physicians prior to the COVID-19 pandemic. Methods: This PRISMA 2020-compliant systematic review and meta-analysis searched EMBASE, APA PsycINFO, and MEDLINE databases for studies published between January 2002 and March 2020 (pre-COVID-19 period). Risk of bias was assessed using a modified Newcastle-Ottawa Scale for cohort and cross-sectional studies. We included studies of physicians where depression/depressive symptoms were measured by either a validated questionnaire or clinical diagnosis. The primary and secondary outcomes measures included assessing the prevalence of depression/depressive symptoms, and whether depression differed by pertinent risk factors (study design, sex, specialty, training stage) in the literature prior to the COVID-19 pandemic. Results: = 98%). Most studies were cross-sectional surveys (n=28) and cohort studies (n=14). A total of 13 different assessment methods were used. We found no statistically significant difference in depression between male and female physicians (OR: 0.78, 95% CI: 0.46, 131), and a slightly increased rates in residents compared to staff physicians [pooled estimates of 36% (95% CI: 26-47%) and 29% (95% CI: 13-53%)]. Finally, 25 studies were deemed "High" risk of bias, while the remaining 17 were "Low" risk. Conclusions: In this review examining depression and depressive symptoms among physicians, we report a pooled estimate of 34% prior to the COVID-19 pandemic. Due to the high degree of heterogeneity in study design and limited examination of key risk factors, limited conclusions can be made regarding the true prevalence across the physicians, and how best to target interventions. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021232814.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".