Classifying and Identifying Motor Learning Behaviors in Voice-Therapy Clinician-Client Interactions: A Proposed Motor Learning Classification Framework
Bibliographic record
Abstract
PURPOSE: We studied whether concepts in motor skill learning could be operationalized to identify clinical interactions and behaviors in a voice therapy setting. Our aim was to test the feasibility of measuring these behaviors in the prepractice phase so that we could eventually evaluate and apply principles of motor learning and skill acquisition to Speech-Language Pathology. Four general categories of behaviors that have been identified in the client-clinician prepractice phase were identified: motivation, modeling, verbal information, and feedback. All variables were extracted from a proposed Motor Learning Classification Framework. METHOD: Nine participants categorized clinician behaviors in three voice therapy training videos into specific, described, prepractice variables. RESULTS: Good intrarater reliability was shown across viewings. Inter-rater reliability was high for modeling and verbal information, but raters were not consistent when identifying behaviors classified as motivation and feedback. Raters responded positively to the classification exercise and the categories encompassed nearly all noted behaviors. CONCLUSION: Behaviors described within the motor learning literature can be identified in the initial stages of voice therapy, providing evidence that motor learning concepts can be used to identify interactions and behaviors in clinical settings. Disagreement in classification among raters was influenced by differences in implicit and explicit interpretations of verbal information. This suggests that greater clarity in specific concepts is needed to support teaching of motor learning principles and implementation of these principles in clinical practice for the treatment of speech-language pathology.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".