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Record W1910796831 · doi:10.1002/pits.21832

CORRELATES OF CONDUCT PROBLEMS AND DEPRESSION COMORBIDITY IN ELEMENTARY SCHOOL BOYS AND GIRLS RECEIVING SPECIAL EDUCATIONAL SERVICES

2015· article· en· W1910796831 on OpenAlexafffund
Martine Poirier, Michèle Déry, Jean Toupin, Pierrette Verlaan, Jean‐Pascal Lemelin, Jadzia Jagiellowicz

Bibliographic record

VenuePsychology in the Schools · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Rimouski
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsComorbidityPsychologyDepression (economics)Clinical psychologyAnxietyAttention deficit hyperactivity disorderConduct disorderIntervention (counseling)Academic achievementChildhood DepressionDevelopmental psychologyLogistic regressionPsychiatryMedicine

Abstract

fetched live from OpenAlex

There is limited empirical research on the correlates of conduct problems (CP) and depression comorbidity during childhood. This study investigated 479 elementary school children (48.2% girls). It compared children with comorbidity to children with CP only, depression only, and control children on individual, academic, social, and family characteristics. The study also analyzed gender differences in the associations between correlates and comorbidity. Multinomial logistic regression results revealed that children with CP and depression had higher levels of anxiety and more school difficulties than children with CP only, more social difficulties and more severe attention deficit and hyperactivity disorder (ADHD) symptoms than children with depression only, and more difficulties in all domains than control children. Girls with CP and depression presented a particularly negative profile, including lower school abilities than CP and control girls, and lower social skills and more severe ADHD symptoms than control girls. Implications for evaluation and intervention planning are discussed.

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 score0.026
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

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

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 head0.055
GPT teacher head0.342
Teacher spread0.288 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

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