Solutions for Bullying: Intervention Training for Pre-service Teachers
Bibliographic record
Abstract
Studies show that teachers lack training and confidence when it comes to intervening effectively in bullying situations. The goal of this study is to respond to the needs of teachers for more formal training on bullying. A two-hour workshop on bullying was developed and offered to pre-service teachers completing a consecutive Teacher Education program in a central-Canadian University. Two parallel questionnaires, each consisting of simulated bullying incidents and standard intervention options, were developed, piloted with a group of experienced teachers, and then used to assess the effect of the workshop on teachers’ responses to the bullying. At pre-test, although three-quarters pre-service teachers in the sample (N = 66) had no formal training in bullying intervention strategies, their selected interventions were rated as consistently appropriate (i.e., restorative and relational) in nature. Study results revealed that pre-service teachers who participated in the workshop showed improved responses to the bullying scenarios, with the greatest improvements evidenced in their intervention with the children in bullying roles. With the growing legal and moral responsibility that educators have to protect their students from bullying, these findings add to accumulating evidence that training in bullying prevention and intervention should be mandatory for pre-service teachers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".