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Record W2785202251 · doi:10.5127/jep.047015

Mother-Child Interpersonal Dynamics: The Influence of Maternal and Child ADHD Symptoms

2015· article· en· W2785202251 on OpenAlexaff
Elizabeth S. Nilsen, Ivana Lizdek, Nicole Ethier

Bibliographic record

VenueJournal of Experimental Psychopathology · 2015
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyHostilityInterpersonal communicationDevelopmental psychologyDominance (genetics)Interpersonal relationshipPsychological interventionClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The relations between maternal and child ADHD symptoms and interpersonal behaviour were examined. Mother-child dyads (N = 59), with children 8- to 12-years-old, exhibiting a range of ADHD symptoms, participated in a problem-solving task. Participants' interpersonal behaviours (along continuums of affiliation: friendliness-hostility and control: dominance-submissiveness) were coded on a continuous moment-to-moment basis, as the interaction unfolded, using a joystick technique. Elevated ADHD symptoms, in both mothers and children, were associated with less overall affiliative interpersonal behaviour. Further, while dyads generally showed complementary behaviour, dyads in which the child had elevated ADHD symptoms demonstrated less complementarity on the affiliation dimension. Finally, the higher the child's ADHD symptoms, the less affiliative and less dominant the mother became over the course of the interaction. Findings highlight ways in which individual differences in ADHD behaviour impact interpersonal functioning and have implications for interventions aimed at enhancing parent-child relationships.

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)0.009
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.335
Teacher spread0.313 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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Same venueJournal of Experimental PsychopathologySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207