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Record W1750337463 · doi:10.1177/070674370304800904

Consequences of Bullying in Schools

2003· review· en· W1750337463 on OpenAlexvenueno aff
Ken Rigby

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2003
Typereview
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPeer victimizationPsychologyInjury preventionOccupational safety and healthSuicide preventionPoison controlHuman factors and ergonomicsLongitudinal studyClinical psychologyDevelopmental psychologyAdolescent healthDistressPeer groupPsychological distressMental healthPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

For the most part, studies of the consequences of bullying in schools have concentrated upon health outcomes for children persistently bullied by their peers. Conclusions have been influenced by how bullying has been conceptualized and assessed, the specific health outcomes investigated, and the research method and data analysis employed. Results from cross-sectional surveys suggest that being victimized by peers is significantly related to comparatively low levels of psychological well-being and social adjustment and to high levels of psychological distress and adverse physical health symptoms. Retrospective reports and studies suggest that peer victimization may contribute to later difficulties with health and well-being. Longitudinal studies provide stronger support for the view that peer victimization is a significant causal factor in schoolchildren's lowered health and well-being and that the effects can be long-lasting. Further evidence from longitudinal studies indicates that the tendency to bully others at school significantly predicts subsequent antisocial and violent behaviour.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.048
GPT teacher head0.336
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations671
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of PsychiatrySame topicBullying, Victimization, and AggressionFrench-language works237,207