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Record W2086073936 · doi:10.3109/17518423.2014.897398

Determinants of self-care participation of young children with cerebral palsy

2014· article· en· W2086073936 on OpenAlexafffund
Doreen J. Bartlett, Lisa A. Chiarello, Sarah Westcott McCoy, Robert J. Palisano, Lynn Jeffries, Alyssa LaForme Fiss, Piotr Wilk

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsCerebral palsyGross motor skillGross Motor Function Classification SystemTest (biology)Structural equation modelingPsychologyAnalysis of varianceHealth careMedicinePhysical therapyMotor skillGerontologyDevelopmental psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To test a model of child, family and service determinants of self-care participation of children with cerebral palsy (CP), grouped by Gross Motor Function Classification System levels (I-II and III-V). METHODS: Participants were a convenience sample of 429 children (242 males) with CP, aged 18-60 months. Data on impairments and gross motor function were collected by reliable therapists; parents provided information about children's health conditions and adaptive behaviour. Seven months later parents reported on family life and services received. One year after study onset, parents documented children's self-care participation. Data from two groups of children were analysed using structural equation modelling. RESULTS: The model explained a significant proportion of the variance of self-care participation, with higher motor function, fewer health conditions and higher levels of adaptive behaviour being associated with greater self-care participation. CONCLUSION: Supporting children's gross motor function, health and adaptive behaviour may optimize self-care participation.

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)0.000
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 score0.020
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations28
Published2014
Admission routes2
Has abstractyes

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