Bullying in adolescents across three years in the context of the COVID-19 pandemic: a repeated cross-sectional and prospective analysis
Bibliographic record
Abstract
BACKGROUND: Bullying has been identified as a risk factor for many issues among adolescents. Although it was already considered a public health issue in Brazil before the COVID-19 pandemic, little is known about how the pandemic and associated public health measures have affected bullying behavior. OBJECTIVE: To explore changes in bullying victimization and perpetration among Brazilian high school students from 2019 to 2022. METHODS: This study utilizes data from the Longitudinal Study of Adolescent Lifestyle (ELEVA), employing a repeated cross-sectional with a nested cohort design. Adolescents (n = 1.987, 50.2% female, mean age 16.4 years) answered a questionnaire and bullying-related information were extracted from two different questions for victims and perpetrators. Multilevel logistic regression models were used. RESULTS: Bullying victimization decreased from 46% (95% CI: 40-52%) in 2019 to 30% (95% CI: 24-36%) in 2022 (OR: 0.46, 95% CI: 0.30-0.69, p < 0.05) in the longitudinal sample. Stable prevalences of bullying victimization (44% in 2019, 40% in 2022, p = 0.090) and perpetration (9.7% in 2019. 8.7% in 2022, p = 0.5) were observed within the repeated cross-sectional sample. CONCLUSION: This study found a significant decrease in bullying victimization among Brazilian high school students from 2019 to 2022 in the longitudinal sample, which coincides with the decrease in bullying behavior associated with getting older. However, the prevalence of bullying victimization and perpetration remained relatively stable and high in the repeated cross-sectional sample. These findings highlight the need for effective policies and interventions to prevent bullying among high school students. Additionally, providing resources and support for students experiencing bullying could be beneficial.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".