Physical abuse of young children during the COVID-19 pandemic: Alarming increase in the relative frequency of hospitalizations during the lockdown period
Bibliographic record
Abstract
BACKGROUND: In France, the COVID-19 pandemic led to a general lockdown from mid-March to mid-May 2020, forcing families to remain confined. We hypothesized that children may have been victims of more physical abuse during the lockdown, involving an increase in the relative frequency of hospitalization. METHODS: Using the national administrative database on all admissions to public and private hospitals (PMSI), we selected all children aged 0-5 years hospitalized and identified physically abused children based on ICD-10 codes. We included 844,227 children hospitalized in March-April 2017-2020, of whom 476 (0.056%) were admitted for physical abuse. Relative frequency of hospitalization for physical abuse observed in March to April 2020 were compared with those from the same months in the three previous years (2017-2019). FINDINGS: Even if absolute number of children exposed to physical abuse did not fluctuate significantly, we found a significant increase in the relative frequency of young children hospitalized for physical abuse from 2017 (0.053%) to 2020 (0.073%). Compared with the 2017-2019 period, and considering the observed decrease in the number of overall hospital admissions during the first lockdown, the number of children exposed to physical violence was 40% superior to what would be expected. INTERPRETATION: The sharp increase in the relative frequency of hospitalizations for physical abuse in children aged 0-5 years in France is alarming. As only the most severe cases were brought to the hospital for treatment during the lockdown, our figures probably only represent the tip of the iceberg of a general increase of violence against young children.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".