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Record W2892509454 · doi:10.1111/sjop.12488

Peer victimization through a trauma lens: Identifying who is at risk for negative outcomes

2018· article· en· W2892509454 on OpenAlexaffabout
Irene K. Hong, Weijun Wang, Debra Pepler, Wendy Craig

Bibliographic record

VenueScandinavian Journal of Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsPeer victimizationPsychologyPsychosocialSituational ethicsPeer groupMental healthDistressContext (archaeology)Clinical psychologyPoison controlSuicide preventionDevelopmental psychologySocial psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Peer victimization is a chronic stressor that occurs within the context of peer interactions and has been robustly associated with numerous negative psychological and social adjustment problems. Although increased frequency of peer victimization has been linked to psychosocial problems, few researchers have studied the role of duration and pervasiveness of victimization (i.e., number of places it occurs). The objective of this study was to examine how frequency, duration, and pervasiveness of peer victimization are associated with youth adjustment. Canadian adolescents (N = 879), ages 12-18 completed an online survey about experiences with peer victimization. Youth also answered questions about internalizing problems, distress, relationship quality with family, friends, and adults in their school and community, as well as academic functioning. Data were analyzed using multinomial logistic regression modeling. Both duration and pervasiveness of peer victimization were predictive of increased internalizing problems, distress, relationship problems, and academic difficulties. Duration and pervasiveness of peer victimization were identified as important factors to consider when predicting youth psychosocial adjustment. By asking questions about these situational factors, parents, teachers, and healthcare providers may more effectively identify youth who are at risk for experiencing mental health problems associated with peer victimization.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.061
GPT teacher head0.391
Teacher spread0.330 · 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.

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

Citations15
Published2018
Admission routes2
Has abstractyes

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