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Record W4289745610 · doi:10.1177/10870547221115874

The Impact of Internalizing Symptoms on Impairment for Children With ADHD: A Strength-Based Perspective

2022· article· en· W4289745610 on OpenAlexaff
Sarah Catherine Bethune, Maria Rogers, David Smith, Jessica Whitley, Michael Hone, Natasha McBrearty

Bibliographic record

VenueJournal of Attention Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsOntario Centre of Excellence for Child and Youth Mental HealthCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsPsychologyFunctional impairmentClinical psychologyStrengths and Difficulties QuestionnaireMental healthPerspective (graphical)Developmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND/PURPOSE: This study aims to investigate the influence of internalizing symptoms on functional impairment for children with ADHD, and whether child strengths and parenting strengths have moderating effects on this relationship. METHODS: Participants included 209 children with ADHD and their caregivers seeking mental health services between the ages of 5 and 11 years. To examine the moderating effects of parenting and child strengths, ordinary least squares regression models were tested using the PROCESS macro for SPSS (v3.5). RESULTS: Results suggest that levels of internalizing symptoms influence functional impairment in children with ADHD. Child strengths moderate the relationship between internalizing symptoms and functional impairment when internalizing symptoms are medium to high. CONCLUSION: Findings from this study demonstrate that facilitating child strengths can help moderate functional impairment for children who experience ADHD and internalizing symptoms.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.331
Teacher spread0.316 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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