Trends in hospitalizations resulting from violent injuries among children and youth in British Columbia during the COVID-19 pandemic
Bibliographic record
Abstract
The increased stressors brought on by the COVID-19 pandemic and its subsequent stay-at-home policies were hypothesized to increase violence and abuse among children and youth. Data on this issue are scant and where they do exist, mixed results have been observed, with variation by jurisdiction. The availability of local data is crucial in assessing the impact of local policies on child violence outcomes. Using hospitalization records with a diagnosis of a violence-related injury, an interrupted time-series design was used to determine whether the initial stay-at-home public health policies affected the rates of violent injury hospitalizations among children and youth aged 0-19 years in British Columbia, Canada. Hospitalization data were obtained from the Discharge Abstract Database from April 1, 2015 to March 31, 2022, with the March 17, 2020 initial lockdown time taken as the interruption point. Effects were examined as the whole cohort, by sex and age-group, and by material deprivation quintiles. There were 745 violence-related injury hospitalizations observed during the study period, with 571 occurring in the pre-COVID period (0.97 per 100,000 population) and 174 in the 24-months post onset of COVID policy restrictions (0.73 per 100,000 population). There was an overall decreasing trend (0.8% per month; 95% CI: 0.2-1.4%) in hospitalization rates during the five-year pre-COVID period. No significant effects were observed at the interruption point or in the two-year follow-up phase. Older males aged 10-19 years (rate ratio: 9.6; 95% CI: 7.7-12.0) and females (rate ratio: 2.1; 95% CI: 1.6-2.7) had higher rates than their younger children aged 0-9 years. Children and youth living in the most materially deprived neighborhoods had 4.3 (95% CI: 3.2-5.6) times the hospitalization rate than those living in least deprived neighborhoods. Neither sex and age-group nor material deprivation quintiles had differential effects on the change at the interruption point or the trend during the follow-up phase. Violence-related injury hospitalization rates among children and youth in British Columbia, Canada have been on a steady decline during the pre-pandemic years. The COVID-19 stay-at-home measures did not appear to have an impact on this rate. • Decreasing trend in violence-related injury hospitalizations in pre-pandemic period • No changes observed at the initial lockdown nor in the two-year follow-up phase • Higher rates among older children and those living in poorer neighborhoods • No differential effects of lockdown policies by age group and deprivation
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".