OA20102. The impact of COVID-19 stay-at-home policies on unintentional injuries among children and youth
Bibliographic record
Abstract
Abstract Methods Hospitalizations and Emergency Department visits for injuries to children and youth were extracted from the National Ambulatory Care Reporting System (NACRS in Ontario) and the Discharge Abstract Database (DAD) in both provinces January 1, 2015 to March 31, 2022. Negative binomial time series was used to model the pre-COVID period and to forecast into the COVID period to calculate the expected number of injury hospitalizations. Observed counts were compared with the expected counts to determine whether the lockdown policies affected the trends of unintentional injuries and where they occurred. Results 15,578 unintentional injury hospitalizations were captured in BC during the study period, while in Ontario there were 37,648. There was a slight reduction in observed unintentional injury hospitalizations in the initial lockdown phase, before returning to pre-pandemic levels. Emergency Department visits went up for some injuries (e.g., bicycle-related), but down for others (e.g., motor vehicle and pedestrian in Ontario). Hospitalization for injuries occurring at home reduced slightly in British Columbia during the initial lockdown phase, then increased to above the expected counts throughout 2021. In Ontario, poisonings at home increased during the lockdown phase. Conclusions Pandemic lockdown measures had an effect on ED visits, most notably in bicycle-related and motor vehicle-related visits. However, there was little effect on the number of unintentional injury hospitalizations, other than the initial stages. Both ED visits and hospitalizations showed changes in where these injuries occurred, with many more injuries occurring at home, with effects lasting through 2021, after pandemic measures had eased. Topic COVID-19 pandemic, Injury trends, Unintentional injuries.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".