Hospitalization for child maltreatment and other types of injury during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The possibility that child maltreatment was misclassified as unintentional injury during the COVID-19 pandemic has not been evaluated. OBJECTIVE: We assessed if child maltreatment hospitalizations changed during the pandemic, and if the change was accompanied by an increase in unintentional injuries. PARTICIPANTS AND SETTING: This study included children aged 0-4 years who were admitted for maltreatment or unintentional injuries between April 2006 and March 2021 in hospitals of Quebec, Canada. METHODS: We used interrupted time series regression to estimate the effect of the pandemic on hospitalization rates for maltreatment, compared with unintentional transport accidents, falls, and mechanical force injuries. We assessed if the change in maltreatment hospitalization was accompanied by an increase in specific types of unintentional injury. RESULTS: Hospitalizations for child maltreatment decreased from 16.3 per 100,000 (95 % CI 9.1-23.4) the year before the pandemic to 13.2 per 100,000 (95 % CI 6.7-19.7) during the first lockdown. Hospitalizations for most types of unintentional injury also decreased, but injuries due to falls involving another person increased from 9.0 to 16.5 per 100,000. Hospitalization rates for maltreatment and unintentional injury remained low during the second lockdown, but mechanical force injuries involving another person increased from 3.8 to 8.1 per 100,000. CONCLUSIONS: Hospitalizations for child maltreatment may have been misclassified as unintentional injuries involving another person during the pandemic. Children admitted for these types of unintentional injuries may benefit from closer assessment to rule out maltreatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".