Reliability of the Revised Motor Learning Strategies Rating Instrument and Its Role in Describing the Motor Learning Strategy Content of Physiotherapy Sessions in Paediatric Acquired Brain Injury
Bibliographic record
Abstract
Purpose: Documenting the use of motor learning strategies (MLS) in physiotherapy is a foundational step in understanding the effectiveness of motor skills–based treatments in paediatric acquired brain injury (ABI). The purpose of this study was to estimate the inter- and intrarater reliability of the revised Motor Learning Strategies Rating Instrument (MLSRI–22) in physiotherapy for children and youth with ABI when administered by trained student physiotherapists. The MLSRI–22 was then used to describe the MLS content of traditional and robotic treadmill training physiotherapy sessions for children with ABI to demonstrate its application. Method: Thirty videos of children with ABI receiving Lokomat or traditional physiotherapy were rated using the MLSRI–22. Inter- and intrarater reliability were estimated using intra-class correlation coefficients (ICCs). Mean MLSRI–22 item scores described the MLS session content. Results: MLSRI–22 total score inter- and intrarater ICCs were 0.81 (95% CI: 0.61, 0.91) and 0.95 (95% CI: 0.90, 0.98), respectively. There were similarities and differences in MLS content between treatment approaches. Conclusions: Trained assessors can reliably administer the MLSRI–22 in physiotherapy for children with ABI. Research using MLSRI–22 scores to explore and systematically compare MLS across treatment approaches may provide insight into their effectiveness and contribute to MLS practice guidelines for children with ABI.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".