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Record W4407184589 · doi:10.1111/jcpp.14120

Research Review: Mechanisms of change and between‐family differences in parenting interventions for children with ADHD – an individual participant data meta‐analysis

2025· review· en· W4407184589 on OpenAlexaff
Constantina Psyllou, Marjolein Luman, Barbara J. van den Hoofdakker, Saskia Van der Oord, Asma Aghebati, Bianca Boyer, Jan K. Buitelaar, Andrea Chronis‐Tuscano, David Daley, Tycho J. Dekkers, George J. DuPaul, Gregory A. Fabiano, Maite Ferrín, Nike Franke, Naama Gershy Tsahor, Elizabeth A. Harvey, Timo Hennig, Sharonne D. Herbert, Pieter J. Hoekstra, Lee Kern, Jennifer A. Mautone, Amori Yee Mikami, Sébastien Normand, Linda J. Pfiffner, Shizuka Shimabukuro, Satyam Antonio Schramm, Julie B. Schweitzer, Margaret H. Sibley, Edmund Sonuga‐Barke, Catherine Thompson, Margaret Thompson, Gail Tripp, Carolyn Webster‐Stratton, Yuhuan Xie, Patty Leijten, Annabeth P. Groenman

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2025
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsInstitut du Savoir MontfortUniversité du Québec en OutaouaisMontfort HospitalUniversity of British Columbia
FundersZonMw
KeywordsPsychologyDevelopmental psychologyPsychological interventionIntervention (counseling)AffectionClinical psychologyAttention deficit hyperactivity disorderMultiple baseline designPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Understanding the mechanisms of change and between-family differences in behavioural parenting interventions for children with attention-deficit/hyperactivity disorder (ADHD) may help personalise interventions. Therefore, we examined whether improvements in parenting are associated with changes in child behaviour and functional outcomes, and how these associations vary based on parents' baseline parenting levels. METHODS: We collected individual participant data including 19 randomised controlled trials focusing on children with ADHD (n = 1,720). Immediate post-intervention measures of child ADHD and oppositional behaviour severity, reported by parents and functional impairment reported by either the parent or probably masked clinicians, were treated as outcomes. We estimated pathways from intervention (vs. control) to child outcomes, via immediate post-intervention parent reports of constructive parenting (e.g. praise), non-constructive parenting (e.g. physical punishment) and parent-child affection (e.g. warmth), while controlling for baseline values of both child outcomes and parenting levels. Baseline values of each parenting variable were used as moderators of the mediated pathways. RESULTS: Improvements in parenting behaviours and parent-child affection immediately following the intervention jointly explained concurrent improvements in children's ADHD severity, oppositional behaviour and functional impairment. Furthermore, when reversing the direction of the pathways, improvements in all child outcomes jointly explained improvements in each aspect of parenting. Improvements in non-constructive parenting and parent-child affection uniquely accounted for intervention effects on functional impairment, especially for families with higher baseline levels of non-constructive parenting. CONCLUSIONS: Our findings might indicate that improvements in both the behavioural and affective aspects of parenting are associated with concurrent reductions in child behaviour problems and functional impairment. However, more research is necessary to explore the potential causal directionality between parenting and child outcomes. Nonetheless, supporting families with poorer parenting skills may be especially important, as reductions in non-constructive parenting in these families are linked to stronger treatment effects on child functional impairment.

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)0.028
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.077
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.044
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.732
GPT teacher head0.565
Teacher spread0.167 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

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