64 COVID-19 policies and inflicted violence injuries among children and youth in Canada
Bibliographic record
Abstract
Background In March 2020, lockdown measures were implemented to curb the spread of COVID-19 infection across Canada. This policy indirectly impacted several health outcomes, including inflicted violence injuries among children and youth. Anecdotally, an increase in cases was hypothesized due to changes in social and family dynamics due to the stay at home policies introduced. Objective The objective of this research was to examine the effect of the initial COVID-19 policy implemented in March 2020 in two provinces in Canada (Ontario, British Columbia). We aimed to describe the change in the number of violence-related hospitalizations among children and youth ages 0 – 19 years. Policy Analysis We used an interrupted time series analysis to examine the change in the number of violence-related hospitalizations before the pandemic (previous to March 2020) with those during the pandemic (after March 2020) in both provinces. Our model examined if implementation of the policy impacted age groups (0–9, 10–19), and sex (male, female), differently. Policy Implications There was a significant, 35% decrease in the rate of hospitalizations (RR: 0.65, 95%CI: 0.52, 0.82) after implementing lockdown policies in the province of Ontario, after controlling for relevant covariates. There was no discernable change in the rate of violence injury hospitalizations in British Columbia. Finally, we report no significant change in hospitalizations by age group or sex when examining interaction effects of the policy. This policy analysis focused on an indirect health impact of lockdown measures on vulnerable populations during the COVID-19 pandemic in Canada. Conclusions There was a significant decrease in the rate of violence-related hospitalizations at implementation of the first COVID-19 policy in Ontario, but this was not observed in British Columbia. We hypothesize that a significant number of injuries did not present to hospital in Ontario during the early days of the COVID-19 pandemic due to the implementation of lockdown measures and fear of COVID-19 infection. Increased surveillance mechanisms to capture and address the number of unreported violence cases in children and youth is required during emergency public health crises.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".