Prevalence of obsessive-compulsive disorders (OCD) symptoms among health care workers in COVID-19 pandemic: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obsessive Compulsive Disorder (OCD) symptoms, are among the serious mental health challenges that Health Care Workers (HCWs) faced during the COVID-19 pandemic. As these symptoms reduce the mental well-being and effectiveness of HCWs which are followed by poor health outcomes for patients, the aim of this systematic review and meta-analysis was to determine the prevalence of OCD symptoms among HCWs worldwide. METHODS: PubMed, Google Scholar, Cochrane, Scopus, Web of Science, ProQuest, Emerald, and ERIC databases were searched using related keywords till the end of October 2021. Observational studies about the prevalence of OCD symptoms among healthcare workers during the COVID-19 pandemic were screened and evaluated. In order to assess the quality of studies, the Newcastle-Ottawa scale (NOS) checklist was used. The effect measure was the prevalence rate with a 95% confidence interval (CI). RESULTS: = 98.6%, P < 0.01), the random effects model was used. The pooled prevalence was 0.29 (95% CI: 0.22-0.38) based on logit transformed CI. CONCLUSIONS: The pooled prevalence of OCD symptoms was 29% among the HCWs during the COVID-19 pandemic. This prevalence was higher than the general population according to the pre-pandemic literature, but lower than the recent reports amid the pandemic. Psychosocial interventions are suggested to be designed and implemented in such conditions.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".