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Record W2159398823 · doi:10.1080/j006v26n01_06

Exploring Assessment Tools and the Target of Intervention for Children with Developmental Coordination Disorder

2006· article· en· W2159398823 on OpenAlexaff
Cheryl Missiuna, Lisa Rivard, Doreen J. Bartlett

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2006
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIntervention (counseling)International Classification of Functioning, Disability and HealthPsychologyContext (archaeology)Physical medicine and rehabilitationDevelopmental psychologyMotor skillActivities of daily livingRehabilitationClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: We reviewed current practice for children with Developmental Coordination Disorder (DCD) using research evidence and the International Classification of Functioning, Disability and Health (ICF). Characteristics of children with DCD can be described at the levels of body function (impairments), whole body movements (activities) and involvement in life situations (participation). SUMMARY OF KEY POINTS: Descriptive instruments measuring the extent of motor impairments or activity limitations can be used to: (1) identify children who might benefit from intervention; and (2) determine the optimal type of intervention and model of service delivery. Evaluative tools that measure activities or participation, but not primary impairments, should be used to determine change over time. Commonly used measures for describing children with DCD and evaluating outcomes are reviewed and discussed in the context of the ICF framework. Intervention approaches are then outlined for children with DCD that are targeted to the levels of activity, participation, and prevention of secondary impairments. CONCLUSIONS: Outcomes of children with DCD will be optimized with the use of current research evidence and the appropriate ICF level guiding both assessment and intervention.

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 score0.155
Threshold uncertainty score0.382

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 head0.047
GPT teacher head0.326
Teacher spread0.279 · 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

Citations65
Published2006
Admission routes1
Has abstractyes

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