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Record W2290789713 · doi:10.1111/1556-4029.13031

Relationship Between Self‐Injurious Behaviors and Levels of Aggression in Children and Adolescents Who Were Subject to Medicolegal Examination,

2016· article· en· W2290789713 on OpenAlexaff
Sait Özsoy, Koray Kara, Hacer Yaşar Teke, Türker Türker, Mehmet Ayhan Cöngöloğlu, Sermet Sezigen, Tulay Renklidag, Mustafa Karapirli, Gulnaz T. Javan

Bibliographic record

VenueJournal of Forensic Sciences · 2016
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsAggressionHostilityPsychologyPoison controlInjury preventionClinical psychologyHuman factors and ergonomicsSuicide preventionVerbal aggressionOccupational safety and healthPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Aggression, which is defined as a behavior causing harm or pain, is a behavioral pattern typically expected in children and adolescents who are involved in criminal activities. The aim of this study was to examine the relationship between aggression and self-injurious behavior (SIB) in children and adolescents. The study was performed in 295 cases which were sent for medicolegal examination. The mean age of the subjects was 14.27 ± 1.05 years (age range 10-18 years). The aggression levels of the subjects were determined using the Aggression Questionnaire (AQ), which is an updated form of the Buss-Durkee Hostility Inventory. The mean total AQ score of the subjects with and without SIB was 78.04 ± 21.0 and 62.75 ± 18.05, respectively (p < 0.01). There were significant statistical differences between the two groups with respect to their subscale scores (p < 0.01). It was concluded that the levels of aggression increased in children and adolescents who were involved in criminal activities when the SIBs increased.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.343
Teacher spread0.303 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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