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Record W2312353074 · doi:10.1017/s0954579414000315

It gets better or does it? Peer victimization and internalizing problems in the transition to young adulthood

2014· article· en· W2312353074 on OpenAlexafffund
Bonnie J. Leadbeater, Kara Thompson, Paweena Sukhawathanakul

Bibliographic record

VenueDevelopment and Psychopathology · 2014
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsPeer victimizationPsychologyYoung adultDepressive symptomsMental healthDevelopmental psychologyInjury preventionClinical psychologyPoison controlAnxietyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Consistent research shows that peer victimization predicts internalizing symptoms in childhood and adolescence, but the extent to which peer victimization and its harmful effects on mental health persists into young adulthood is unclear. The current study describes patterns of physical and relational victimization during and after high school, and examines concurrent and prospective associations between internalizing symptoms (depressive and anxious symptoms) and peer victimization (physical and relational) from adolescence to young adulthood (ages 12-27). Data were collected from the Victoria Healthy Youth Survey, a five-wave multicohort study conducted biennially between 2003 and 2011 (N = 662). Physical victimization was consistently low and stable over time. Relational victimization increased for males after high school. Both types of victimization were associated concurrently with internalizing symptoms across young adulthood for males and for females. Although sex differences were important, victimization in high school also predicted increases in internalizing problems over time.

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)0.003
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.281
Teacher spread0.265 · 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

Citations56
Published2014
Admission routes2
Has abstractyes

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