Use of Motor Learning Strategies in Occupational Therapy for Children and Youth with Acquired Brain Injury
Bibliographic record
Abstract
Motor learning strategies (MLS) can be used to promote motor skills acquisition in children and youth with acquired brain injury (ABI). While occupational therapists (OTs) likely use MLS in clinical practice, research has not investigated the extent and variety of their application.Aims: This study explored MLS use by OTs in pediatric ABI and factors influencing their application.Method: Individual video-recorded occupational therapy sessions for a sample of eight children/youth (ages 4-16) with ABI were evaluated via mixed methods approach. The Motor Learning Strategies Rating Instrument (MLSRI-22) quantified the extent of MLS use in each video. Directed content analysis of the videos explored the factors influencing how and when MLS were applied.Results: The most frequently used MLS were promoting problem solving, encouragement, directing attention to the body, permitting errors as part of learning, repetitive practice, and whole practice. Three themes described how and when the OTs used MLS: 1) Getting buy-in, 2) Going with the flow, and 3) Movement and thinking go hand-in-hand.Conclusions: The OTs frequently used MLS with children with ABI, appearing to select MLS based on factors related to the child, task, and environment. These findings are fundamental to future exploration of OT decision-making and evaluation of MLS effectiveness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".