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Record W2028879958 · doi:10.1097/pep.0b013e3181beb09d

The Application of Motor Learning Strategies Within Functionally Based Interventions for Children with Neuromotor Conditions

2009· review· en· W2028879958 on OpenAlexaff
Danielle Levac, Laurie Wishart, Cheryl Missiuna, F. Virginia Wright

Bibliographic record

VenuePediatric Physical Therapy · 2009
Typereview
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMotor learningIntervention (counseling)Psychological interventionMotor skillPsychologyGeneralizationCognitionTask (project management)Cognitive psychologyDevelopmental psychologyNeuroscienceEngineering

Abstract

fetched live from OpenAlex

In Brief Purpose: To identify and describe the application of 3 motor learning strategies (verbal instructions, practice, and verbal feedback) within 4 intervention approaches (cognitive orientation to daily occupational performance, neuromotor task training, family-centered functional therapy, and activity-focused motor interventions). Methods: A scoping review of the literature was conducted. Two themes characterizing the application of motor learning strategies within the approaches are identified and described. Results: Application of a motor learning strategy can be a defining component of the intervention or a means of enhancing generalization and transfer of learning beyond the intervention. Often, insufficient information limits full understanding of strategy application within the approach. Conclusions: A greater understanding of the application, and perceived nonapplication, of motor learning strategies within intervention approaches has important clinical and research implications. The authors examined the literature on 4 motor learning approaches to determine the extent to which each approach incorporated specific motor-learning strategies. Their review suggests the need for additional research on the use of motor-learning strategies with children.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.041
GPT teacher head0.356
Teacher spread0.315 · 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 designOther design
Domainnot available
GenreReview

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

Citations62
Published2009
Admission routes1
Has abstractyes

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