Reliability of the Motor Learning Strategies Rating Instrument in physiotherapy intervention for children with cerebral palsy
Bibliographic record
Abstract
AIM: To evaluate the reliability of the Motor Learning Strategies Rating Instrument (MLSRI-20) in gait-based, video-recorded physiotherapy interventions for children with cerebral palsy (CP). METHOD: Thirty videos of 18 children with CP, aged 6 to 17 years, participating in either traditional or Lokomat-based physiotherapy interventions were rated using the MLSRI-20. Physiotherapist raters provided general and item-specific feedback after rating each video, which was used when interpreting reliability results. RESULTS: Both interrater and intrarater reliability of the MLSRI-20 total score was good. The interrater reliability intraclass correlation coefficient (ICC) was 0.78 with a 95% confidence interval (CI) of 0.53-0.89 and a coefficient of variation (CV) of 11.8%. The intrarater reliability ICC was 0.89 with a 95% CI of 0.76-0.95 and CV of 7.8%. Rater feedback identified task delineation and interpretation of therapist verbalizations as sources of interrater reliability-related scoring challenges. INTERPRETATION: The MLSRI-20 is a reliable tool for measuring the extent to which a physiotherapist uses motor learning strategies during a video-recorded intervention. These results have clinical and research implications for documenting and analyzing the motor learning content of physiotherapy interventions for children with CP. WHAT THIS PAPER ADDS: The Motor Learning Strategies Rating Instrument (MLSRI-20) is reliable for use by trained physiotherapist raters. Measuring motor learning strategies can identify active 'ingredients' in physiotherapy interventions for children with cerebral palsy. The MLSRI-20 promotes a common language in motor learning.
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".