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Record W4410707776 · doi:10.15173/child.v3i1.3900

Understanding Sex-Based Differences in Childhood Conduct Disorder

2025· article· en· W4410707776 on OpenAlexaboutno aff
Sarah Allam, Roohi Devje, Linda Duong, Nimra Hooda, Samantha Rutherford

Bibliographic record

VenueThe Child Health Interdisciplinary Literature and Discovery Journal · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyConduct disorderDevelopmental psychology

Abstract

fetched live from OpenAlex

Externalizing symptoms are behaviours that violate major social norms, such as aggression, violation of rules, or deceitfulness. Conduct disorder (CD) is a mental disorder defined by patterns of externalizing behaviour. Recent studies find a significant discrepancy in the rates of childhood diagnosis between sexes, with a higher prevalence for boys compared to girls. Consequently, it has been suggested that current diagnostic criteria may not fully capture the nuanced manifestation of CD in girls. This paper aims to explore theories of sex differences in CD and implications for diagnostic criteria as described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Through a review of the literature, this study examines childhood sex-specific differences in the symptoms, subtypes, comorbidities, and neurobiological correlates of CD. Theories discussed include the gender paradox, delayed onset pathway, and familial perspective theories. Based on sex-specific findings, implications for screening and diagnosis are discussed. In addition, the suggestion of advocating for further research on modifications in DSM criteria and use of sex-specific risk assessment tools is included. Research in this area has the potential to challenge misconceptions surrounding sex, gender, and externalizing behaviour with the goal of improving outcomes for Canadian youth with CD.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.949

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 studies0.0010.000
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 head0.052
GPT teacher head0.344
Teacher spread0.293 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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