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Record W1966096732 · doi:10.1080/15388220.2011.602599

Understanding How Programs Work to Prevent Overt Aggressive Behaviors: A Meta-analysis of Mediators of Elementary School–Based Programs

2011· article· en· W1966096732 on OpenAlexaff
Allison B. Dymnicki, Roger P. Weissberg, David B. Henry

Bibliographic record

VenueJournal of School Violence · 2011
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsInstitute of Health Services and Policy Research
FundersAmerican Psychological Association
KeywordsAggressionPsychologyMediationPoison controlDevelopmental psychologyHuman factors and ergonomicsSocial cognitive theoryCognitionTest (biology)Injury preventionClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Several recent meta-analyses of universal school-based violence prevention studies indicate the overall positive impacts of these approaches on aggression. These studies, however, assess impacts on broadly defined measures of aggression. Furthermore, little research has analyzed the mechanisms through which these programs attempt to reduce overt aggressive behavior. The current study analyzed overall impacts on a more narrowly defined outcome—overt aggressive behavior—and identified associated mediators in 36 universal prevention studies conducted with kindergarten through fifth-grade students. Programs were associated with a significant, although small, reduction in overt aggression behavior. Three types of mediators were identified: measures of skill acquisition, social-cognitive processes, and classroom characteristics. Using MacKinnon's joint significance test to test for mediation, four measures of skill acquisition, two measures of social cognitive, and one measure of classroom characteristics were identified as significant mediating variables. Implications for the design of effective violence prevention programs and mediators to assess in future research are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.144
GPT teacher head0.338
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2011
Admission routes1
Has abstractyes

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