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Record W2897151363 · doi:10.1177/0886260518805103

The Role of Life Satisfaction in Predicting Youth Violence and Offending: A Prospective Examination

2018· article· en· W2897151363 on OpenAlexafffund
Katherine B. Hanniball, Jodi L. Viljoen, Catherine S. Shaffer, Gira Bhatt, Roger G. Tweed, Lara B. Aknin, Nathalie Gagnon, Kevin S. Douglas, Stephen Dooley

Bibliographic record

VenueJournal of Interpersonal Violence · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsKwantlen Polytechnic UniversitySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLife satisfactionModerationPsychologyMediationSuicide preventionPoison controlInjury preventionHuman factors and ergonomicsClinical psychologyDevelopmental psychologySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Life satisfaction in adolescence has been shown to protect against numerous negative outcomes (e.g., substance use, sexual risk-taking), but limited work has directly explored the relationship between life satisfaction and youth violence and offending. As such, we conducted a prospective assessment to explore this relationship among community ( n = 334) and at-risk youth ( n = 99). Findings suggest life satisfaction is significantly associated with decreased offending and violence within both samples and adds incremental value above established risk factors in predicting violent and total offending among community youth. Furthermore, moderation analyses indicate that the protective value of life satisfaction is greater for youth with high callous–unemotional traits. Mediation analyses suggest that youth who are unsatisfied with their lives may seek out substance use, in turn elevating risk of offending. Together, these findings indicate that efforts to improve overall life satisfaction may help prevent adolescent offending. However, future research is needed.

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)0.001
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 score0.136
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.020
GPT teacher head0.305
Teacher spread0.285 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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