Unpacking the black box of voice therapy: A clinical application and revision of the Motor Learning Classification Framework (MLCF)

Voice therapy is a complex behavioural intervention. Understanding its components is integral for continued advancement of voice therapy research, translation of evidence into the clinical setting and improved client care. The Motor Learning Classification Framework (MLCF) offers an excellent opportunity for increasing such knowledge, specifically in relation to identifying variables that affect motor learning (ML), an important mechanism hypothesised to bring about voice change during voice therapy. The MLCF has shown promising results in identifying speech-language pathologists’ (SLPs) use of ML variables during experimentally controlled voice therapy contexts. The purpose of this study was to test the feasibility of applying the framework in the clinical context of everyday voice therapy practice. Data consisted of two video-recorded voice therapy sessions representing usual voice therapy care. Classification of ML variables used by SLPs during the recorded sessions was attempted based on the MLCF. Several problematic features of the framework were identified. Based on deliberations between the authors of the current paper, the MLCF was revised using an iterative process. This resulted in the construction of an updated version of the framework (MLCF-V2). The MLCF-V2 organises ML strategies into two broad categories: directly observable behaviours and learning processes. The framework incorporates greater consideration of theory and empirical evidence supporting motivational, attentional focus and subjective error estimation influences on ML. Several examples of each ML variable are included as well as an attempt to provide clearer classification instruction. It is anticipated that the MLCF-V2 will provide a more useful and reliable classification for use in future investigations of SLPs’ use of ML variables during usual voice therapy practice.