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Record W2041335174 · doi:10.1080/13811110802572098

Bullying Increased Suicide Risk: Prospective Study of Korean Adolescents

2009· article· en· W2041335174 on OpenAlexaff
Young S. Kim, Bennett Leventhal, Yun‐Joo Koh, W. Thomas Boyce

Bibliographic record

VenueArchives of Suicide Research · 2009
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSuicide preventionPoison controlInjury preventionSuicide RiskPsychiatryProspective cohort studyOdds ratioMedicinePsychologyOccupational safety and healthHuman factors and ergonomicsClinical psychologyMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

This study examines the independent impact of bullying on suicide risk. Bullying was assessed by peer nomination in a prospective study of 1,655 7th and 8th grade Korean students, and suicide by youth self-report. Odds Ratios (ORs) of bullying for suicidal risks were computed, controlling for other suicide risk factors. Victim-Perpetrators and female Victims at baseline showed increased risk for persistent suicidality (OR: 2.4-9.8). Male Incident Victims exhibited increased risk for suicidal behaviors and ideations (OR = 4.4, 3.6). Female Persistent Perpetrators exhibited increased risks for suicidal behaviors; male Incident Perpetrators had increased risk for suicidal ideations (OR = 2.7, 2.3). Baseline-only male Victim-Perpetrators showed increased risk for suicidal ideations. (OR = 6.4). Bullying independently increased suicide risks.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.384
Teacher spread0.333 · 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 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

Citations173
Published2009
Admission routes1
Has abstractyes

Explore more

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