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Improving Motor Proficiency in Children with Developmental Delays: A Meta-Analysis Evaluating the Impact of Motor Skills Interventions

2024· article· en· W4391223212 on OpenAlexvenueno aff
Maha Siddiqui, Sumaira Imran Farooqui, Jaza Rizvi, Bashir Ahmed Soomro, Batool Hassan

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionWorld Health Organization
KeywordsMeta-analysisMotor skillPsychological interventionPsychologyDevelopmental psychologyGross motor skillMedicine

Abstract

fetched live from OpenAlex

This study investigated the impact of motor skill interventions in improving motor proficiency among children with developmental delays following Preferred Reporting Items for Systematic Reviews and Meta-analysis “PRISMA” recommendations. The included studies were searched on four databases: Google Scholar, PEDro, MEDLINE, and Cochrane Library. Studies published during the year 2012 to 2022 were selected. The data was extracted by defining the publication year, type of study design, targeted population, and type of physical therapy intervention. The outcome measures included four components of motor proficiency: bilateral coordination, balance, speed and agility, and strength. The results revealed statistically significant findings and a large effect size for bilateral coordination (SMD=1.003, CI=95%) and speed and agility (SMD=0.854, CI=95%). However, a smaller effect size with significant findings was observed in the balance domains (SMD=0.333, CI=95%) and strength (SMD=0.337, CI=95%). Despite the promising results of the analyzed interventions, some of the included studies observed a high risk of bias. However, it is evident from the analysis that protocols directed toward advanced approaches have shown more promising results than traditional physical exercise regimens.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.386
Teacher spread0.302 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intellectual Disability - Diagnosis and TreatmentSame topicChildren's Physical and Motor DevelopmentFrench-language works237,207