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Record W2141586994 · doi:10.1111/1469-7610.00739

Prevalence of Psychiatric Diagnoses and the Role of Perceived Impairment: Findings from an Adolescent Community Sample

2001· article· en· W2141586994 on OpenAlexaff
Elisa Romano, Richard E. Tremblay, Frank Vitaro, Mark Zoccolillo, Linda S. Pagani

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2001
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityMontreal Children's HospitalUniversité de Montréal
Fundersnot available
KeywordsPsychologyMedical diagnosisPsychiatric diagnosisPsychiatryClinical psychologySample (material)Developmental psychologySchizophrenia (object-oriented programming)Medicine

Abstract

fetched live from OpenAlex

The present study examined psychiatric functioning in a community sample of adolescents aged 14 to 17 years (average age of 15 years). We administered the Diagnostic Interview Schedule for Children-2.25 (DISC-2.25) to 1,201 adolescents and their mothers to obtain prevalence estimates of DSM-III-R disorders and the amount of perceived impairment associated with these disorders. While adolescent females reported a significantly higher prevalence of psychiatric disorders than males (15.5% vs. 8.5%), mothers indicated no sex difference. Compared with adolescent males, females had significantly higher rates of internalizing, anxiety. and depressive disorders. In contrast, the prevalence of externalizing disorders was significantly higher among adolescent males. The inclusion of an impairment criterion had a significant impact in reducing the prevalence rates of overall psychiatric disorders. This reduction occurred mainly through impairment's effects on internalizing disorders, specifically anxiety-based disorders (i.e., simple and social phobia). Given the limited research on the effect of impairment on the prevalence of adolescent psychiatric disorders, future work in this area seems warranted.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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

Citations154
Published2001
Admission routes1
Has abstractyes

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