Enhancing Social Responsibility and Prosocial Leadership to Prevent Aggression, Peer Victimization, and Emotional Problems in Elementary School Children
Bibliographic record
Abstract
Testing the theories that form the basis of prevention programs can enhance our understanding of behavioral change and inform the development, coordination, and adaptation of prevention programs. However, theories of change showing the linkages from intervention program components to risk or protective factors to desired outcomes across time are rarely specified or tested. In this 2-year longitudinal study, we test the theory that increases in two protective factors (i.e., children's prosocial leadership and their teachers' expectations of social responsibility) targeted by the WITS Programs (Walk Away, Ignore, Talk it Out, and Seek Help) would be associated with declines in peer victimization, aggression, and emotional problems. Participants included Canadian students, in grades 1-4 at baseline (n = 1329) and their parents and teachers. Consistent with our theory of change, variability in program implementation (adherence and integration) and in children's use of program skills (child responsiveness) are related to increases in both protective factors. Increases in these protective factors are associated with subsequent declines in children's aggression, victimization, and emotional problems. We discuss how enhancement of these protective factors may operate to improve child outcomes and the need for theory-based research to refine and improve the effectiveness of intervention strategies and to improve program scale-up.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".