Consequences of the COVID-19 Pandemic on Children's Mental Health: A Meta-Analysis
Bibliographic record
Abstract
Background: The COVID-19 pandemic has exacerbated mental health problems in many individuals, including children. Children with pre-existing socio-demographic or developmental risk factors may be particularly vulnerable to the negative effects of the pandemic and associated public health preventive measures. Objective: This systematic review and meta-analysis explored the impacts of the COVID-19 pandemic on the mental health of children aged 5–13 years-old, while highlighting the specific difficulties experienced by children with neurodevelopmental issues or chronic health conditions. Methods: A systematic search of the published literature was conducted in Medline, ERIC, PsycINFO, and Google Scholar, followed by a quantitative meta-analysis of the eligible studies. Results: Out of the 985 articles identified, 28 empirical studies with prospective or retrospective longitudinal data were included in the quantitative synthesis. COVID-19 lockdown measures were associated with negative general mental health outcomes among children ( g = 0.28, p < 0.001, and k = 21), but of small magnitude. Sleep habits were also changed during the pandemic, as sleep duration significantly increased in children ( g = 0.32; p = 0.004, and k = 9). Moreover, results did not differ between children from the general population and those from clinical populations such as children with epilepsy, oncology, neurodevelopmental disorders, or obesity. Effect sizes were larger in European vs. Asian countries. Conclusions: Studies included in this review suggest that children's mental health was generally negatively impacted during the COVID-19 pandemic. More research is needed to understand the long-term effects of the COVID-19 pandemic on children's mental health and the influence of specific risks factors as they evolve over time.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.057 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".