Inflicted violence-related injuries among children and youth in Ontario during the COVID-19 pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, it was hypothesized that stay-at-home policies would impact cases of violence, abuse, and neglect among children and youth due to increased familial stressors. We examined the effect of the implementation of pandemic policies on violence-related emergency department (ED) visits and hospitalizations among youth. and Setting: Violence-related ED visits and hospitalizations among children and youth ages 0–19 in Ontario, Canada were obtained from April 2015 until the most recent available date, March 31, 2022. We used an interrupted time series design to model the change in monthly violence-related ED visits and hospitalizations before and during the pandemic. We used negative binomial models to estimate the immediate effect of the policy and the change in the number of injuries during the pandemic. After adjusting for seasonality and population changes over our study period, we observed a 56% decrease in violence-related ED visits (RR: 0.44, 95%CI: 0.38, 0.50) and a 35% decrease in hospitalizations (RR: 0.65, 95%CI: 0.52, 0.82) immediately after the implementation of the pandemic policy, followed by moderate increasing trends. We observed no difference in the rate of violence-related ED visits and hospitalizations by sex, age or material deprivation; however, males aged 10–19 years and those in higher quintiles of material deprivation had higher average rates of injuries compared to females, those in younger age groups and lower quintiles of deprivation over the study period. We observed an abrupt decrease in the rate of violence-related ED visits and hospitalizations immediately after the onset of pandemic policies in Ontario. Following this, violence-related injuries increased, approaching pre-pandemic levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".