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Record W2782154834

Épidémiologie des troubles psychiatriques en pédopsychiatrie Epidemiology of psychiatric disorders paediatric psychiatry

2005· article· fr· W2782154834 on OpenAlexaff
Éric Fombonne, Ste-Catherine West

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychiatryEpidemiologyAnxietyBipolar disorderSchizophrenia (object-oriented programming)Depression (economics)PsychologyEating disordersPrevalence of mental disordersAnorexia nervosaAttention deficit hyperactivity disorderAutismDysthymic DisorderConduct disorderEpidemiology of child psychiatric disordersMedicineClinical psychologyMajor depressive disorderMood
DOInot available

Abstract

fetched live from OpenAlex

This article reviews findings from epidemiological surveys of psychiatric disor- ders in children and adolescents. Prevalence estimates, and gender and age correlates are provided and, when available, incidence data. We start with findings from major surveys that yielded estimates of global morbidity due to psychiatric disorders in youth. Hen, a detailed review of epidemiological findings is provided on a disorder by disorder basis, including: autism and pervasive developmental disorders, attention deficit hype- ractivity disorder (ADHD), anxiety disorders, obsessive compulsive disorders, affective disorders including major depression and dysthymic disorder, anorexia and bulimia nervosa, schizophrenia and bipolar disorder. We conclude with estimates of numbers of children affected with a psychiatric disorder in France. A conservative figure is that 1 out of 8 children is suffering from a psychiatric condition at any time. © 2005 Elsevier SAS. Tous droits reserves.

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)0.002
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.343
Teacher spread0.304 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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