School Closure and Child Maltreatment During the COVID-19 Pandemic
Bibliographic record
Abstract
It is not known how school closure affected child maltreatment. We conducted a retrospective cohort, linear mixed-models study of 133 counties (comprising 8,582,479 children) in Virginia between 2018 and 2021. Exposure was the opening of schools at least 2 days a week. Outcomes were referrals and incidence of child maltreatment reported to the Department of Social Services. In 2020-2021, there were descriptively more referrals (in-person: 50.9 per 10,000 [95% CI: 47.9, 54.0]; virtual: 45.8 per 10,000 [95% CI: 40.7, 50.9]) and incidence (in-person: 3.7 per 10,000 [95% CI: 3.3, 4.2]; virtual: 2.9 per 10,000 [95% CI: 2.3, 3.5]) of child maltreatment in counties with in-person schooling, though these differences did not reach statistical significance. The referral rate variations (between pandemic and pre-pandemic eras) of counties with in-person schooling was significantly greater than rate changes in counties with virtual schooling during the summer period. There were no differences in incidence in any quarter. Higher poverty within a county was associated with both higher referrals and incidence. Our findings suggest that child maltreatment is driven primarily by underlying differences in counties (namely, poverty) rather than the type of schooling children receive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".