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Record W2041993601 · doi:10.5539/ass.v8n9p92

Substance Abuse and Aggressive Behavior among Adolescents

2012· article· en· W2041993601 on OpenAlexvenueno aff
I. Fauziah, Mahadzirah Mohamad, Sheau Tsuey Chong, Azmi Abd Manaf

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Health and Behaviors
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsAggressionClinical psychologyPsychologySubstance abuseExploratory researchRehabilitationJuvenileJuvenile delinquencyPsychiatry

Abstract

fetched live from OpenAlex

Social workers, psychologists and psychopharmacologists have devoted little attention to study the direct relationship between drugs and adolescents’ aggression. The main objective of this study was therefore to determine the extent of the level of aggressive behavior among adolescents who underwent rehabilitation of drug abuse. This study also sought to find out the relationship between type of drugs used with aggressive behavior among adolescents. Respondents were 200 adolescents from three juvenile Henry Gurney schools in Malaysia who took part in this exploratory cross-sectional survey research design. A set of questionnaire was constructed by the researcher based on the Aggression Questionnaires (AQ) scale. Results showed that the majority of adolescents (95 percent) indicated an aggressive behavior of moderate to high level. The result of the study also found that adolescents who have been using heroin (r = 0.016, p <0.05) and morphine drugs (r = 0.181, p <0.05) showed significant correlation with aggressive behavior. The findings provided evidence for the significant role of the goverment to enhance rehabilitation modules for adolescents involved in drug abuse. Education on how to effectively deal with aggressive behavior among adolescents at risk should be emphasized for building positive behavior among adolescents in order to produce potential young generation in the future.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.417
Teacher spread0.370 · 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.

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

Citations18
Published2012
Admission routes1
Has abstractyes

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