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Record W2206033842 · doi:10.1080/15388220.2015.1095100

What Limits the Effectiveness of Antibullying Programs? A Thematic Analysis of the Perspective of Teachers

2015· article· en· W2206033842 on OpenAlexafffund
Charles E. Cunningham, Heather Rimas, Stephanie Mielko, Cailin Mapp, Lesley J. Cunningham, Don H. Buchanan, Tracy Vaillancourt, Yvonne Chen, Ken Deal, Madalyn Marcus

Bibliographic record

VenueJournal of School Violence · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of OttawaHamilton Health SciencesMcMaster University
FundersCanadian Institutes of Health ResearchMinistère de l’Éducation, Gouvernement de l’Ontario
KeywordsEnthusiasmThematic analysisAmbivalencePsychologyPoison controlCurriculumPerspective (graphical)Suicide preventionBest practiceHuman factors and ergonomicsCompromiseMedical educationFeelingLimitingInjury preventionFocus groupApplied psychologyPedagogySocial psychologyMedicineQualitative researchSociologyPolitical scienceMedical emergencyEngineering

Abstract

fetched live from OpenAlex

Prevention programs yield modest reductions in bullying in North American schools. This study explored the perspective of educators regarding factors limiting the impact of these initiatives. Transcripts from nineteen 90-min focus groups with 103 educators were coded thematically. Educators felt that off-site incidents, cyberbullying, and the growing involvement of boys in psychologically aggressive incidents have increased the complexity of bullying. Curriculum demands limit time for training, implementation, and prompt responses to bullying. Principals failing to back teachers up, ambivalent colleagues, uncooperative parents, and a lack of evidence reduce their commitment to implementation. Promising programs are discontinued in favor of new initiatives. Some educators modified programs; others, feeling frustrated and discouraged, struggled to mobilize the enthusiasm needed to ensure successful implementation. Dealing with bullying in the face of limited time, training, and support may increase emotional exhaustion and compromise program effectiveness.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.339
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations54
Published2015
Admission routes2
Has abstractyes

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