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Record W4413023378 · doi:10.1016/j.humov.2025.103389

The utility of the MABC-2 for measuring motor impairment in children with ADHD: Examining measurement invariance in children with and without symptoms of ADHD

2025· article· en· W4413023378 on OpenAlexafffund
Matthew Bourke, Matthew Kwan, Kathryn Fortnum, Martín O’Flaherty, Sara King‐Dowling, John Cairney

Bibliographic record

VenueHuman Movement Science · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcMaster UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsPsychologyMeasurement invarianceAudiologyMotor impairmentAttention deficit hyperactivity disorderDevelopmental psychologyPhysical medicine and rehabilitationClinical psychologyMedicineConfirmatory factor analysisStructural equation modelingStatistics

Abstract

fetched live from OpenAlex

It is important to screen for motor impairments in ADHD due to high comorbidity, yet little is known about the validity of established and standardized motor assessment batteries in children with symptoms of ADHD. Therefore, this study aimed to determine the utility of using the Movement Assessment Battery for Children - 2nd Edition (MABC-2) in children aged 7-9 years with symptoms of ADHD. To achieve this, measurement invariance of the MABC-2 was examined between children with and without symptoms of ADHD. A total of 479 children (n = 277 boys, n = 387 white, n = 66 with ADHD), participated in this study. Children were classified as having ADHD through parental report on the Conner's Parent Rating Scales. Measurement invariance was assesses using a multi-group CFA. A three correlated factor model (Manual Dexterity, Throwing and Catching, Balance) fit the data extremely well (RMSEA = 0.030, SRMR = 0.030, CFI = 0.987, TLI = 0.981) and configural, metric, scalar, and partial strict invariance was demonstrated between children with and without ADHD. These results provide evidence to support the use of the MABC-2 to assess motor impairments in children with symptoms ADHD.

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)0.002
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 score0.006
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.307
Teacher spread0.245 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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