Changes in Depression and Anxiety Among Children and Adolescents From Before to During the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: There is a growing body of high-quality cohort-based research that has examined changes in child and adolescent mental health during the COVID-19 pandemic vs before the pandemic. Some studies have found that child and adolescent depression and anxiety symptoms have increased, while others have found these symptoms to have remained stable or decreased. Objective: To synthesize the available longitudinal cohort-based research evidence to estimate the direction and magnitude of changes in depression and anxiety symptoms in children and adolescents assessed before and during the pandemic. Data Sources: Medline, Embase, and PsycInfo were searched for studies published between January 1, 2020, and May 17, 2022. Study Selection: Included studies reported on depression and/or anxiety symptoms, had cohort data comparing prepandemic to pandemic estimates, included a sample of children and/or adolescents younger than 19 years, and were published in English in a peer-reviewed journal. Data Extraction and Synthesis: In total, 53 longitudinal cohort studies from 12 countries with 87 study estimates representing 40 807 children and adolescents were included. Main Outcomes and Measures: Standardized mean changes (SMC) in depression and anxiety symptoms from before to during the pandemic. Results: The analysis included 40 807 children and adolescents represented in pre-COVID-19 studies and 33 682 represented in during-COVID-19 studies. There was good evidence of an increase in depression symptoms (SMC, 0.26; 95% CI, 0.19 to 0.33). Changes in depression symptoms were most conclusive for study estimates among female individuals (SMC, 0.32; 95% CI, 0.21 to 0.42), study estimates with mid to high income (SMC, 0.35; 95% CI, 0.07 to 0.63), and study estimates sourced from North America (SMC, 0.25; 95% CI, 0.15 to 0.36) and Europe (SMC, 0.35; 95% CI, 0.17 to 0.53). There was strong evidence that anxiety symptoms increased slightly during the pandemic (SMC, 0.10; 95% CI, 0.04 to 0.16), and there was some evidence of an increase in study estimates with mid to high income. Conclusions: This systematic review and meta-analysis of longitudinal studies including children and adolescents found an increase in depression symptoms during the COVID-19 pandemic, particularly among female individuals and those from relatively higher-income backgrounds.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".