Prevalence of mental health symptoms in children and adolescents during the COVID‐19 pandemic: A meta‐analysis
Bibliographic record
Abstract
The COVID-19 pandemic and its accompanying infection control measures introduced sudden and significant disruptions to the lives of children and adolescents around the world. Given the potential for negative impacts on the mental health of youths as a result of these changes, we conducted a systematic review and meta-analysis to examine the prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances in children and adolescents during the pandemic. We searched major literature databases for relevant cross-sectional or longitudinal studies that included primary and secondary school students or children and adolescents ≤18 years of age. Prevalence values were extracted, logit-transformed, and pooled. Based on 191 included studies with 1,389,447 children and adolescents, we found the pooled prevalence of depressive symptoms, anxiety symptoms, and sleep disturbances to be 31%, 31%, and 42%, respectively. Age, grade levels, education levels, gender, geographical regions, and electronics use were correlated with the prevalence of mental health symptoms. The prevalence of mental health symptoms also increased with time, although signs of recovery and stabilization were also observed. Overall, the results from this review demonstrate the need for increased mental health research, monitoring, and intervention for children and adolescents during the current and future pandemics.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".