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Record W2023865847 · doi:10.3109/01942638.2012.662584

The Assessment of Preschool Children's Participation: Internal Consistency and Construct Validity

2012· article· en· W2023865847 on OpenAlexaff
Mary Law, Gillian King, Theresa Petrenchik, Marilyn K. Kertoy, Dana Anaby

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2012
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsWestern UniversityHolland Bloorview Kids Rehabilitation HospitalMcGill UniversityMcMaster University Medical CentreMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsCerebral palsyPsychologyGross motor skillInternal consistencyRecreationDevelopmental psychologyMotor skillConstruct validityChild developmentPsychometrics

Abstract

fetched live from OpenAlex

Participation in activities provides the means for young children to learn, play, develop skills, and develop a sense of personal identity. The Assessment of Preschool Children's Participation (APCP) is a newly developed measure to capture the participation of children aged 2 to 5 years and 11 months in the areas of play, skill development, active physical recreation, and social activities. Data from a clinical trial involving 120 children with cerebral palsy indicated that the APCP has moderate to very good internal consistency. The measure distinguishes between children below or above 4 years of age across levels of the Gross Motor Classification System, and between income levels below or above the median regional income range. The APCP, with a focus on preschool children, has potential use for assessment and identification of activity areas in which the child is participating and areas in which participation may be restricted.

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)0.014
metaresearch head score (Gemma)0.020
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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.374
Teacher spread0.328 · 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

Citations72
Published2012
Admission routes1
Has abstractyes

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