Extrinsic feedback and upper limb motor skill learning in typically-developing children and children with cerebral palsy: Review
Bibliographic record
Abstract
BACKGROUND: Improvment of upper limb motor skills occurs through motor learning that can be enhanced by providing extrinsic feedback. Different types and frequencies of feedback are discussed but specific guidelines for use of feedback for motor learning in typically-developing (TD) children and children with Cerebral Palsy (CP) are not available. OBJECTIVE: Identify the most effective modalities and frequencies of feedback for improving upper limb motor skills in TD children and children with CP. METHODS: Ovid MEDLINE, Cochrane, PEDro and PubMed-NCBI were searched from 1950 to December 2015 to identify English-language articles addressing the role of extrinsic feedback on upper limb motor learning in TD children and children with CP. Nine studies were selected with a total of 243 TD children and 102 children with CP. Study quality was evaluated using the Downs and Black scale and levels of evidence were determined with Sackett's quality ratings. RESULTS: There was a lack of consistency in the modalities and frequencies of feedback delivery used to improve motor learning in TD children and in children with CP. Moreover, the complexity of the task to be learned influenced the degree of motor learning achieved. CONCLUSION: A better understanding of the influence of feedback on motor learning is needed to optimize motor skill acquisition in children with CP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".