RESUMO
Guided Imagery and Music (GIM) is a range of therapeutic practices in which clients listen to music selected by a trained practitioner with the aim of gaining cognitive insight through evoked imagery that may be beneficial in working through various inner experiences, pain, or trauma. It is crucial to this process that the chosen music is tailored to the client's therapeutic goals and receptiveness. Wärja and Bonde [(2014). Music as co-therapist: Towards a taxonomy of music in therapeutic Music and Imagery work. Music and Medicine, 6(2), 16-27.] developed a taxonomy consisting of nine categories of musical-psychological characteristics and constructs (e.g., tempo, instrumentation, and mood) aligning with various therapeutic contexts (e.g., supporting and exploring) for helping GIM practitioners select appropriate music; however, its reliability has never before been assessed. In this paper, we present a listening study carried out with 63 GIM therapists and trainees, in order to measure the inter-rater agreement in (1) classifying 10 randomly selected pieces from 30 into one or more categories of the Wärja and Bonde [(2014). Music as co-therapist: Towards a taxonomy of music in therapeutic Music and Imagery work. Music and Medicine, 6(2), 16-27.] taxonomy, and (2) identifying for each piece heard one or more adjectives from the Hevner mood wheel that best characterize it. Our results indicate participants who utilized all categories but with slight to fair overall agreement; however, largely moderate agreement was reported for less musically complex pieces as well as across all pieces when considering only the three primary categories. Our findings not only support the continued use of the taxonomy and mood for helping select GIM music but also suggest the possible need for clearer descriptions in its subcategories and further training of practitioners who employ it in practice.
RESUMO
BACKGROUND: Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. METHODS: Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. RESULTS: 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. CONCLUSION: The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.