A Systematic Review on Primary School Teachers’ Characteristics and Behaviors in Identifying, Preventing, and Reducing Bullying
Bibliographic record
Abstract
Abstract Despite the expanding body of research on school bullying and interventions, knowledge is limited on what teachers should do to identify, prevent, and reduce bullying. This systematic literature review provides an overview of research on the role of primary school teachers with regard to bullying and victimization. A conceptual framework was developed in line with the Theory of Planned Pehavior, which can serve in further research to facilitate research in investigating the prevention and reduction of bullying. Different elements of this framework were distinguished in categorizing the literature: teachers’ knowledge, attitudes, perceived subjective norms, and self-efficacy, which impacted subsequently the likelihood to intervene, used strategies and programs, and ultimately the bullying prevalence in the classroom. In total, 75 studies complied to the inclusion criteria and were reviewed systematically. The Newcastle–Ottawa Quality Assessment was used to assess the quality of each study, leading to 25 papers with an adequate research design that were discussed in more detail. The approach in this review provides a framework to combine studies on single or multiple elements of a complex theoretical model of which only some parts have been empirically investigated.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".