Children in a Pandemic: The Impact of Isolation and Economic Recession on the Health of Youth
Bibliographic record
Abstract
The COVID-19 pandemic started in China in late 2019 and has since caused social and economic disruption across the globe. While children seem to be less severely affected by the disease, indirect factors may be affecting children’s physical and mental wellbeing. Countries have adopted social distancing policies, including school closures, in an attempt to slow the spread of the disease. Children were forced to be socially isolated from agemates during prolonged home-stay at an unprecedented scale. In addition, the pandemic is causing economic decline surpassing that of the Great Recession in 2008-2009. Other than some preliminary reports, it is unknown how the current situation will affect child health. To estimate how these drastic changes may impact children, we examine previous studies on social isolation and economic recession. Social isolation in the context of infectious disease is understudied, but the limited literature does show a correlation with increased post-traumatic stress scores and need for mental health services. Studies from the 2008 recession show how economic decline may be related to higher rates of infant and child mortality, obesity, mental health concerns, suicide related behaviours and child maltreatment. Potential increases in child maltreatment are of concern because reporting has decreased in a variety of jurisdictions after the onset of the pandemic. Parents and those who work with children should be aware of these indirect consequences during and after the pandemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".