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Record W29282234

Detecting Related Message Traffic

2004· article· en· W29282234 on OpenAlexaff
David B. Skillicorn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceInternet privacyComputer securityAdvertisingPsychologySocial psychologyBusiness
DOInot available

Abstract

fetched live from OpenAlex

Substance-related disorders (SRD) are common psychiatric morbidities among adolescents within youth correctional systems. Identification and treatment of SRDs is critical for successful reformation and reintegration. Lack of simple, structured, valid, brief screening instruments that can be easily administered and scored by lay workers militates against screening for SRDs. We present the results of the reliability and concurrent validity of the CRAFFT (acronym for Car, Relax, Alone, Forget, Friends, and Trouble) substance abuse screening instrument among residents of youth correctional facilities in Lagos, Nigeria. Adolescents who screened positive on CRAFFT were further assessed with the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) to determine whether they met diagnostic criteria for SRDs. The mean CRAFFT scores for all the adolescents (<i>n</i> = 178) was 0.66 (SD ± 1.45). A total of 23 (12.9%) had CRAFFT scores of >1.00. The CRAFFT instrument has good internal consistency (Cronbach's α = 0.85) and 2-week test reliability (Spearman correlation = 0.979; <i>p</i> < .001). At a cutoff point of >1.00, CRAFFT had the best sensitivity and specificity (area under the curve = 0.889; 95% confidence interval 0.765-1.000) among the participants. As validated, the CRAFFT is a reliable instrument for screening for SRDs in incarcerated youth.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations8
Published2004
Admission routes1
Has abstractyes

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