The Parallel Pandemic: A Systematic Review on the Effects of the COVID-19 Pandemic on OCD among Children and Adolescents
Bibliographic record
Abstract
The COVID-19 pandemic and the accompanying social changes severely impacted mental health globally. Children and adolescents may have been vulnerable to adverse mental health outcomes, especially obsessive-compulsive disorder (OCD), due to their underdeveloped resilience and coping skills stemming from their progressing physical and psychological development. Few studies have explored the parallels between the pandemic and OCD trends in this population. This systematic review aims to identify the impacts of COVID-19 on OCD among children and adolescents. Using the PRISMA guidelines, a systematic search of eight databases for studies that assessed OCD outcomes independently or as part of other psychiatric diagnoses during the COVID-19 pandemic was conducted. The search was limited to studies on humans and those written in English and published between January 2020 and May 2023. We identified 788 articles, out of which 71 were selected for a full-text review. Twenty-two papers were synthesized from 10 countries for the final analysis. We found that 77% of our studies suggested that the COVID-19 pandemic had a negative impact on OCD among children and adolescents. We also found a complex interplay of individual, household, and socio-structural factors associated with the aggravation of OCD. Conversely, a few studies revealed that the pandemic strengthened relationships and resilience. The findings of this study emphasize the need for mental health screening and support for this population, especially during pandemic periods.
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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 | 0.006 | 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".