UNDERSTANDING SCHOOL VIOLENCE: REVIEW OF CHARACTERISTICS, ASSOCIATED FACTORS, AND INTERVENTIONS
Bibliographic record
Abstract
ABSTRACT: Educational institutions should be welcoming and safe environments for promoting student learning and development. However, safety in schools has been compromised by a variety of incidents of school violence, ranging from peer victimization and bullying to armed attacks. This study undertakes a rapid review of systematic reviews on school violence conducted in Brazil and abroad, aiming to understand the characteristics of school violence, its associated factors, armed attacks in schools, and possible prevention and intervention measures. A rapid review approach was adopted, following the PRISMA protocol (Page et al., 2020), with an emphasis on systematic reviews on school violence, which were screened by searching titles in eleven databases. A total of 1,738 publications were retrieved, and after excluding duplicates, screening references, and assessing the quality of the full texts, 127 studies were included in the analysis. The categories resulting from the analysis were: characteristics of school violence (15% of the studies), factors associated with school violence (45.7%), interventions in response to school violence (33.8%), and armed attacks on schools (5.5%). The results highlighted the complexity of school violence, the heterogeneity of school violence prevalence rates, and the need for more research into school violence in the Brazilian context to develop and evaluate inclusive and culturally appropriate interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".