School bullying before and during COVID‐19: Results from a population‐based randomized design
Bibliographic record
Abstract
We examined the impact of COVID-19 on bullying prevalence rates in a sample of 6578 Canadian students in Grades 4 to 12. To account for school changes associated with the pandemic, students were randomized at the school level into two conditions: (1) the pre-COVID-19 condition, assessing bullying prevalence rates retrospectively before the pandemic, and (2) the current condition, assessing rates during the pandemic. Results indicated that students reported far higher rates of bullying involvement before the pandemic than during the pandemic across all forms of bullying (general, physical, verbal, and social), except for cyber bullying, where differences in rates were less pronounced. Despite anti-Asian rhetoric during the pandemic, no difference was found between East Asian Canadian and White students on bullying victimization. Finally, our validity checks largely confirmed previous published patterns in both conditions: (1) girls were more likely to report being bullied than boys, (2) boys were more likely to report bullying others than girls, (3) elementary school students reported higher bullying involvement than secondary school students, and (4) gender diverse and LGTBQ + students reported being bullied at higher rates than students who identified as gender binary or heterosexual. These results highlight that the pandemic may have mitigated bullying rates, prompting the need to consider retaining some of the educational reforms used to reduce the spread of the virus that could foster caring interpersonal relationships at school such as reduced class sizes, increased supervision, and blended learning.
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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 | 0.044 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".