Obsessive-Compulsive Disorder During the COVID-19 Pandemic—A Systematic Review
Bibliographic record
Abstract
Background: The COVID-19 pandemic and its associated restrictions may contribute to a deterioration in mental health; individuals with obsessive-compulsive disorder (OCD) may be particularly affected. This systematic review aimed to investigate the effects of the current pandemic on people diagnosed with OCD, and whether pandemics may affect the development of OCD symptoms. Methods: We conducted a systematic search using NCBI PubMed, SCOPUS, and Google Scholar on February 9, 2021. Research articles related to OCD and COVID-19 or other pandemics were attempted to be identified using pre-defined search terms. Case reports, clinical guidelines, letters, and clinical research articles including ≥100 participants were included; reviews were excluded. The systematic review adheres to PRISMA guidelines and the Newcastle-Ottawa Scale was used to assess the quality of the included clinical research articles. Results: A total of 79 articles were included in the full-text assessment. Of these, 59 were clinical research articles, two were clinical guidelines, six were case reports, and 12 were letters. The research articles examined OCD symptoms in adult patients with diagnosed OCD, the general population, pregnant women, healthcare workers, students, and young adults, children, and adolescents. Only one study on OCD in previous pandemics was identified. Conclusion: This systematic review found that people both with and without diagnosed OCD prior to the pandemic generally experienced a worsened landscape of symptoms of OCD during the COVID-19 pandemic. However, the responses are heterogeneous and many factors other than the pandemic seemed to affect the development of OCD symptoms. To prevent the impairment of symptoms and the development of new cases, close monitoring of patients with OCD and education of the general public is essential. Literature is still limited; thus, multinational and cross-cultural, longitudinal studies are warranted to gain further insights on this topic.
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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) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".