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Record W4383815017 · doi:10.1037/dev0001582

Impacts of psychopathic traits dimensions on the development of indirect aggression from childhood to adolescence.

2023· article· en· W4383815017 on OpenAlexafffund
Stéphanie Boutin, Vincent Bégin, Michèle Déry

Bibliographic record

VenueDevelopmental Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsGrandiosityPsychologyPreadolescenceAggressionPsycINFODevelopmental psychologyNarcissismImpulsivityPsychopathyPoison controlSocial psychologyPersonalityMedicine

Abstract

fetched live from OpenAlex

= 370; 40.3% girls) were referred to school-based services for conduct problems (CP) at study intake. Latent class growth analyses revealed four developmental trajectories of IA, which were regressed on psychopathic traits dimensions using a three-step approach. After adjusting for demographic confounders, CP, and other dimensions of psychopathic traits, only narcissism-grandiosity traits significantly predicted memberships to a high and stable trajectory of IA use. The associations between the other dimensions of psychopathic traits and IA trajectories were not significant when considering confounders. No moderating effects by child sex were observed. These results suggest that narcissism-grandiosity traits could be of use for clinicians aiming to detect children most at risk of showing high and persistent levels of IA. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.002
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.342
Teacher spread0.291 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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