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Record W2134633745 · doi:10.1093/jpepsy/jst078

Gene–Environment Processes Linking Peer Victimization and Physical Health Problems: A Longitudinal Twin Study

2013· article· en· W2134633745 on OpenAlexafffund
Mara Brendgen, Alain Girard, Frank Vitaro, Ginette Dionne, Richard E. Tremblay, Daniel Pérusse, Michel Boivin

Bibliographic record

VenueJournal of Pediatric Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsTwin studyPeer victimizationPsychologyPoison controlOccupational safety and healthHuman factors and ergonomicsSuicide preventionInjury preventionDevelopmental psychologyGeneticsEnvironmental healthMedicineBiologyHeritability

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined whether (a) a genetic disposition for physical health problems increases the risk of peer victimization and (b) peer victimization interacts with genetic vulnerability in explaining physical health problems. METHODS: Participants were 167 monozygotic and 119 dizyogtic twin pairs. Physical symptoms were assessed in early childhood and early adolescence. Peer victimization was assessed in middle childhood. RESULTS: Genetic vulnerability for physical health problems in early childhood was unrelated to later peer victimization, but genetic vulnerability for physical health problems during early adolescence increased the risk of victimization. Victimization did not interact with genetic factors in predicting physical symptoms. Environmental, not genetic, factors had the greatest influence on the development of physical symptoms in victims. CONCLUSION: Genetic vulnerability for physical health problems in early adolescence increases the risk of peer victimization. Whether victims suffer a further increase in physical symptoms depends on the presence of protective environmental factors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.333
Teacher spread0.301 · 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

Citations18
Published2013
Admission routes2
Has abstractyes

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