Association of COVID-19 School Closures and Youth Mental Health: A Global Perspective
Bibliographic record
Abstract
Background: Adolescence is a formative developmental period for establishing social, emotional, and behavioural habits essential for mental health.The COVID-19 pandemic disrupted these processes globally, with school closures affecting over 91% of the world's student population.This study examines the association between school closures and adolescent mental health, focusing on the moderating roles of household income and access to learning modalities.Methods: A repeated cross-sectional analysis of 13,350 adolescents from 120 countries was conducted using data from the Gallup World Poll, UNICEF, and UNESCO.Mental health was measured using two 100-point multi-item scales, the Daily Experience index which measured positive wellbeing, and Life Evaluation which measured life satisfaction.Linear mixed models assessed relationships between school closures, household income, and access to learning modalities.Results: Full school closures were negatively associated with positive wellbeing (-32.77points) and life satisfaction (-15.18points).Adolescents from higher-income households also exhibited higher positive wellbeing and life satisfaction, with each doubling of income associated with a +3.39-point difference in positive well-being and a +2.14-point difference in life satisfaction.Access to learning modalities mitigated the negative impacts of full school closures, increasing positive well-being by 14.02 points and life satisfaction by 4.23 points, with greater benefits observed among wealthier adolescents.Females scored 1.62 points lower on positive wellbeing but 1.21 points higher on life satisfaction compared to males.Conclusion: These findings highlight structural inequities in the mental health impacts of school closures, emphasizing the need for equitable access to educational resources, digital infrastructure, and targeted mental health support.Future research should explore social support systems, longterm effects, and the experiences of younger and non-binary youth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".