Impact of lockdown and school closure on children’s health and well-being during the first wave of COVID-19: a narrative review
Bibliographic record
Abstract
Background: In the context of containment measures against the COVID-19 pandemic, the aims were to examine the impact of lockdown and school closures on childs' and adolescents' health and well-being and social inequalities in health. Methods: Literature review by searching five databases until November 2020. We included quantitative peer-reviewed studies reporting health and well-being outcomes in children (0-18 years) related to closure measures' impact due to COVID-19. A pair of authors assessed the risk of bias of included studies. A descriptive and narrative synthesis was carried out. Findings: Twenty-two studies, including high-income, middle-income and low-income countries, fulfilled our search criteria and were judged not to have an increased risk of bias. Studies from Australia, Spain and China showed an increase in depressive symptoms and decrease in life satisfaction. A decrease in physical activity and increase in unhealthy food consumption were shown in studies from two countries. There was a decrease in the number of visits to the emergency department in four countries, an increase in child mortality in Cameroon and a decrease by over 50% of immunisations administered in Pakistan. A significant drop of 39% in child protection medical examination referrals during 2020 compared with the previous years was found in the UK, a decrease in allegations of child abuse and neglect by almost one-third due to school closures in Florida, and an increase in the number of children with physical child abuse trauma was found in one centre in the USA. Interpretation: From available reports, pandemic school closure and lockdown have adverse effects on child health and well-being in the short and probably long term. We urge governments to take the negative public health consequences into account before adopting restrictive measures in childhood.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".