The Role of Self-Assessed Teacher’s Efficacy in Assessing the Severity of Violence, Predicting Interventions, and Choosing Strategies in Cases of Peer Violence
Bibliographic record
Abstract
Peer violence is a common, prevalent, almost inevitable phenomenon in all schools, so more and more often, for the purpose of suppressing it, attention is paid to the factors that predict teachers' response to violence, and among them self-efficacy stands out. The aim of this paper is to examine the predictive role of perceived teacher self-efficacy in assessing the severity of violence, predicting interventions, and selecting strategies in cases of peer violence. The research was conducted on a representative sample (N = 639) of primary school teachers in the Republic of Croatia, with average age 43 (sd = 10,599). Data were collected via Vignettes and the Self-Efficiency Scale, along with the Socio-Demographic Characteristics Questionnaire. Research results show that teachers with a higher perception of self-efficacy are more inclined to assess violence more seriously and are more likely to intervene, and those who assess violence more seriously will intervene more often. Self-efficacy is also a significant predictor of the likelihood of intervention, and the perception of the severity of violence has proven to be a significant moderator of that relationship. Teachers with low perceived self-efficacy and low assessment of the severity of violence have the lowest probability of intervention, and teachers with high levels of self-efficacy and high assessment of the severity of violence have the highest probability. Teachers who choose cooperative strategies have the highest levels of assessed self-efficacy, while those who are prone to non-intervention show the lowest levels of self-efficacy. In conclusion, we discuss implications for teacher training and their professional development.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".