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Record W4322495463 · doi:10.1111/josh.13300

Socioeconomic Differences in the Association Between Bullying Behaviors and Mental Health in Canadian Adolescents

2023· article· en· W4322495463 on OpenAlexafffundabout
Kana Yokoji, Nour Hammami, Frank J. Elgar

Bibliographic record

VenueJournal of School Health · 2023
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsMcGill University Health CentreTrent UniversityDurham CollegeMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsPublic Health AgencyPublic Health Agency of Canada
KeywordsSocioeconomic statusPovertyAssociation (psychology)Mental healthSuicide preventionPsychologyPoison controlInjury preventionOccupational safety and healthLife satisfactionClinical psychologyHuman factors and ergonomicsMedicineEnvironmental healthPsychiatrySocial psychologyPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Bullying and poverty are each associated with poor health in adolescents. We examined socioeconomic differences in the association of bullying and health. METHODS: The 2017/2018 Canadian Health Behaviour of School-aged Children study surveyed 21,750 youth (9-18 years). We used linear regression models to investigate interactive effects of bullying involvement (traditional and cyberbullying) and socioeconomic position (SEP) on self-reported life satisfaction, psychological symptoms, and physical symptoms. RESULTS: Involvement in either form of bullying, as a perpetrator or a target, was associated with worse health and well-being compared to uninvolved youths. Associations of victimization via conventional bullying with low life satisfaction (b = -.33 [-.61, .05]), more psychological symptoms (b = .83 [.27, 1.38]), and more somatic symptoms (b = .56 [.14, .98]) were stronger at lower SEP. CONCLUSION: Socioeconomic disadvantage intensifies the association between bullying victimization and poor health. The intersections of victimization and poverty pose a significant health risk to adolescents.

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)0.003
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 score0.027
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.036
GPT teacher head0.349
Teacher spread0.314 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

Explore more

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