The Application of Motor Learning Strategies Within Functionally Based Interventions for Children with Neuromotor Conditions
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".