Professional freedom : A grounded theory on the use of music analysis in psychodynamic music therapy

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Professional freedom : A grounded theory on the use of music analysis in psychodynamic music therapy © 2018 GAMUT – The Grieg Academy Music Therapy Research Centre Published version Letule, Nerdinga; Ala-Ruona, Esa; Erkkilä, Jaakko Letule, N., Ala-Ruona, E., & Erkkilä, J. (2018). Professional freedom : A grounded theory on the use of music analysis in psychodynamic music therapy. Nordic Journal of Music Therapy, 27(5), 448-466. https://doi.org/10.1080/08098131.2018.1490920 2018 Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=rnjm20 Nordic Journal of Music Therapy ISSN: 0809-8131 (Print) 1944-8260 (Online) Journal homepage: http://www.tandfonline.com/loi/rnjm20 Professional freedom: A grounded theory on the use of music analysis in psychodynamic music therapy Nerdinga Letulė, Esa Ala-Ruona & Jaakko Erkkilä To cite this article: Nerdinga Letulė, Esa Ala-Ruona & Jaakko Erkkilä (2018) Professional freedom: A grounded theory on the use of music analysis in psychodynamic music therapy, Nordic Journal of Music Therapy, 27:5, 448-466, DOI: 10.1080/08098131.2018.1490920 To link to this article: https://doi.org/10.1080/08098131.2018.1490920 © 2018 GAMUT – The Grieg Academy Music Therapy Research Centre Published online: 06 Sep 2018. Submit your article to this journal Article views: 233 View Crossmark data ORIGINAL RESEARCH ARTICLE Professional freedom: A grounded theory on the use of music analysis in psychodynamic music therapy Nerdinga Letulė a , Esa Ala-Ruona a and Jaakko Erkkilä a a Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, Finland ABSTRACT Although music is the fundamental element of music therapy, music analysis methods are a particularly under-researched area. This study investigates how and when psychodynamically orientated music therapists employ the analysis of musical material in both clinical work and research. Constructivist grounded theory was employed in the collection and analysis of the data. Eight participants, all highly experienced in psychodynamic music therapy, were recruited using referral sampling. In-depth interviews focused on therapists’ experiences of working with different client groups, and the applicability of different assessment methods. Strauss and Corbin’s coding paradigm was used to determine causal and intervening conditions, action strategies and the consequences of music analysis. Professional freedom (a tension between creative forces and professional responsibilities) emerged as the most important factor influencing the method, application and frequency of music analysis. Therapists used either explicit knowledge (model-based theoretical understanding and reductionist action strategy), or implicit knowledge (context-based theoretical understanding and holistic action strategy) or used a combination of both approaches. Implicit knowledge was found to lessen the ability to give an account of analytical processes, but increased sensitivity to clients’abilities and needs, while explicit knowledge led to frustration about interdisciplinary disagreement, greater excitement about discovery and increased workloads. ARTICLE HISTORY Received 15 May 2017; Accepted 21 May 2018 KEYWORDS Music analysis; grounded theory; psychodynamic music therapy Introduction Music is the essential mode in music therapy. It enables both communication and interaction with the client. Yet, despite its key importance to the therapeutic process, Bonde (2016b) notes that “music therapy literature includes surprisingly and disappointingly few studies with a focus on music itself”(p. 105). The author continues by comparing the music in music therapy to a black box –a complex system whose internal workings are not readily understood. To date, while some research has been carried out (Bonde, 2016a), there remains very little scientific understanding of the music in music therapy. Musicologists, by contrast, have been analysing music for centuries, and research from this field is also relevant to the music therapy setting (Ansdell, 1997;Rolsvjord,2006). CONTACT Nerdinga Letulė[email protected] Department of Music, Art and Culture studies, University of Jyväskylä, Jyväskylä FI-40014, Finland © 2018 GAMUT –The Grieg Academy Music Therapy Research Centre This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecom mons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. NORDIC JOURNAL OF MUSIC THERAPY 2018, VOL. 27, NO. 5, 448–466 https://doi.org/10.1080/08098131.2018.1490920 Meyer (1989) introduced the distinction between the primary and secondary parameters of music. Primary parameters, such as meter and harmony, have discrete, proportional relationships (up-beat and down-beat, or tonic and dominant). Secondary parameters, such as dynamics and timbre, do not have the capacity to form musical syntax, because the changes in these parameters are relative (softer and louder, darker and brighter, etc.). Primary parameters, because of organizational complexity, take time to perceive, while secondary parameters are perceived instantaneously. But, as Eitan and Granot (2009)point out, “while the perception and cognition of secondary parameters, even in a musical context, may be chiefly based upon general auditory experience (or perhaps even on innate tendencies), processing primary parameters relies chiefly on music-specific exposure”(p. 144). In music therapy, this music-specific exposure is not a commonality amongst the client population; therefore, musicological analyses that focus on primary parameters are not sufficient to account for the musical processes taking place in therapy. In the music therapy literature, three music analysis methods have been documented: aural (Abrams, 2007; Baxter et al., 2007; Forinash & Gonzalez, 1989;Keith,2007; Mahoney, 2010), notation-based (Bergstrøm-Nielsen, 1993; Gilboa & Bensimon, 2007; Lee, 2000) and computational (Erkkilä, Lartillot, Luck, Riikkilä, & Toiviainen, 2004; Hunt, Kirk, Abbotson, & Abbotson, 2000; Streeter et al., 2012). Aural music analysis is the most subjective method of the three, and arguably the most prevalent in music therapy practice. Nordoff–Robbins scales (Nordoff & Robbins, 1977) and later contributions (Aigen, 2014; Guerrero & Turry, 2012), Bruscia’s Improvisation Assessment Profiles (Bruscia, 1987;Gardstrom,2004), Bruscia’s method of analysing GIM music (Bruscia, 1996) and Wigram’s quantitative use of IAP (1999,2000) are amongst the most influential aural analysis methods. Notation-based analysis is performed after transcribing music into either a traditional or a graphical score. Because of the skill and attention to detail required in the transcription of clinical improvisations, this method is the most time consuming of the three (Gilboa, 2012), but could enable a very detailed examination of musical processes. Computational methods promise great benefits in terms of efficiency, but currently lack links between computationally retrieved results and clinically relevant issues (De Backer, 2008). At the present developmental stage, algorithms cannot achieve pattern-recognition results comparable to those of notation-based analyses and cannot identify secondary parameter changes as successfully as aural analysis (Letulė, Brabant, Thompson, & Erkkilä, 2015). Furthermore, very few of these methods have been subjected to rigorous testing for validity and reliability (Sabbatella, 2004). With the exception of the Streeter surveys (2010), which included questions about music therapy evaluation methods, to date there has been no research on how music therapists use these diverse music analysis methods in their practice. The objective of the present study is to explore how and when psychodynamically oriented music therapists use music analysis in practice. The term psychodynamic music therapy refers herein to the “existence of, and dynamic processes in, an unconscious mind, which has an influence on intrapsychic and interpersonal processes within and outside of the musical activity between the therapist and patient” (Metzner, 2016, p. 448). A psychodynamic music therapist seeks to enable a client to communicate his/her inner state through musical expression. A researcher, then, needs to reject absolutism in favour of referentialism (i.e., a belief that music is capable of communicating extramusical meanings) (Meyer, 1956). Readers should bear in mind that this investigation does not seek to recommend a single best approach to music analysis, but rather to explore multiple perspectives and to reveal NORDIC JOURNAL OF MUSIC THERAPY 449 the causalities and consequences of different action strategies in music therapy practice. The current study aims to determine factors influencing music therapists’ approaches to music analysis and explore how available music analysis methods are being utilised in practice, and, as a result, to create the grounds for future studies by identifying key categories for qualitative as well as quantitative research and to guide the development of subsequent music analysis methods. Method We chose a constructivist grounded theory approach for the present study in order to capture the complexities of the phenomenon (Charmaz, 2006,2014; Mills, Bonner, & Francis, 2006). Since grounded theory building is entirely inductive, it was not affected by the lack of previous literature on the topic (Daveson, 2016; O’Callaghan, 2016). Although, according to grounded theory principles, a literature review was performed as the last step of the research process, we were already aware of eminent authors in this field, and used this knowledge to select informants for the present study. We made a list of psychodynamically oriented music therapists that had developed music analysis methods or published research that employed music analysis methods. All key informants were, or had been, involved in clinical, educational and research work and had no less than 10 years of professional experience. In order to form a comprehensive theory, we chose therapists that used various music analysis methods and worked with diverse client populations. Though we attempted to recruit informants from a wider geographical area, not all invitations to participate were answered, and the eight who participated were all based in Europe. In qualitative research, findings are generated rather than discovered, therefore it was very important to be reflexive on the entire research context (Gentles, Jack, Nicholas, & McKibbon, 2014; Mruck & Mey, 2007). We removed terminology from the interview questions that might have led participants, and attempted to prevent our professional interests from affecting the way we viewed or worked with data. Although we are involved in the activities of the Finnish Centre for Interdisciplinary Music Research, apply an Integrative Improvisational Music Therapy approach (Erkkilä et al., 2011), and use computational music analysis tools such as MIR Toolbox (Eerola & Toiviainen, 2004; Lartillot & Toiviainen, 2007) and Music Therapy Toolbox (Erkkilä, 2007), the current research is equally interested in all possible approaches to and outcomes of analysing music in psychodynamic music therapy. According to the National Advisory Board on Research Ethics in the country where the current study was carried out, this type of research did not require ethical review. The present study was based on interviews with a small sample of experts, and the data was analysed by one researcher only. Consequently, it lacked triangulation within and between methods (Carter, Bryant-Lukosius, DiCenso, Blythe, & Neville, 2014; Flick, 2004). More generally, this qualitative study had a relatively broad research question with little previous research having been undertaken on the topic, so it was challenging for informants to answer, and –because of the diversity of opinions and experiences –difficult for researchers to conceptualize without being too simplistic (Wheeler & Bruscia, 2016). It should also be noted that interviews were conducted in English, and none of the informants were native English speakers. The most challenging aspect of the current study was the process of raising the analytical 450 N. LETULĖET AL. level from description to abstraction. Participants voiced very different, sometimes opposing, opinions on approaches to music analysis and it was difficult to account for all variation within a single core concept. The subjects, material, design, and analysis methods varied depending on the phase of the study. Phase 1 –Exploration –identified the most salient themes (initial coding), and facilitated the design of the questionnaire for Phase 2. In Phase 2 –Theoretical Sampling –new interview data were collected and coded (initial, axial, selective coding) until the point of saturation was reached. In Phase 3 –Refinement –the theory was presented in order to clarify and validate it. We employed two styles of interaction with participants: in Phases 1 and 3 interviews were interactive and discussion-like, while in Phase 2 the approach was based on open, non-directive listening to informants. Although Phase 3 discussion did not result in any new categories, it was an important part of validation. As Strauss and Corbin (1990) said, “opening up one’s analysis to the scrutiny of others helps guard against bias. Discussions with other researchers often lead to new insights and increased theoretical sensitivity”(p.11). Phase 1 –exploration Participant Key informant 1, coded as Mr A, was a Finnish music therapist with 29 years of experience (at the time of interviewing, February 2015). Mr A was selected because his research interests (as seen from research publications) were the most similar to the research question of the current study. Procedure Interview data were collected in person over two meetings, which lasted a total of 3.5 h. Materials Mr A answered 49 questions about music recording techniques, music analysis, analysis interpretation, communication with clients and colleagues, the use of different assessment tools, and issues in relating assessment results to clinical issues. Because interviews were lengthy and rich in data, we decided that these were sufficient for the first stage of the current study. Data analysis Transcripts of the interviews with Mr A were analysed with initial coding. We used the in-vivo technique (labelling the data only with participant’sownwords) to stay as close to the participant’s point of view as possible. This phase was used to identify different practices in music analysis and highlight areas of inconsistency and debate. Phase 2 –theoretical sampling Participants Six participants were interviewed during Phase 2 from March to May 2015 (see Table 1). In addition to being music therapists, some had training in music performance, music education, composition, dance therapy, psychotherapy, and psychoanalysis. NORDIC JOURNAL OF MUSIC THERAPY 451 Procedure Interviews were conducted in person where possible or via Skype, and lasted on average for 62 min. Materials The main questions addressed to each participant related to music making (four questions), music analysis (three questions), interpretation of analysis results (three questions), and a finalopenquestionthatinquiredwhetherthere was anything else the participant considered to be important regarding music analysis in music therapy that had not been discussed in the interview. As seen in Appendix 1, the questions were very open: no music parameters (e.g., rhythm, melody) or music analysis methods (e.g., aural, computational) were identified in the questions in order to avoid leading. If participants found a question confusing and asked for clarification, then suggestions (marked as a. b. c. in Appendix 1)were presented to them. Clarifying and probing questions were asked with the aim of explicating or expanding upon opinions and experiences, which necessarily varied for each participant. Data analysis Coding was completed in three stages. In the first stage, open coding was used to identify concepts, define their properties, and start grouping them into categories. Two techniques were used in open coding –first line-by-line in-vivo coding, followed by process coding. During process coding (Saldana, 2009), all the gerunds (verbs ending “–ing”) and transitional indicators (“if”,“when”,“because”, etc.) were highlighted and subsequently used to generate new codes or establish code properties. While many categories discussed in the results correspond with individual interview questions (e.g., therapists’background, client populations), some emerged directly from data (e.g., creativity, bodily experiences). Axial coding was the second stage of data analysis, which focused on the connections between categories by clustering the data. Following Strauss and Corbin’s coding paradigm (Strauss &Corbin,1990), causal conditions, intervening conditions, action strategies and consequences were identified. Selective coding was the final stage, which was used to integrate all the findings into a coherent picture. Conditions, strategies, and consequences were interrelated, and a core category emerged as the best explanation for the observed phenomenon. Phase 3 –refinement Once it was judged that saturation point had been reached in Phase 2 and the theory was formulated, an oral presentation was given at the 10th European Music Therapy Conference in Vienna (Letulė, Thompson, & Erkkilä, 2016). To begin with, the first author gave a 30-min presentation explaining the methodology and results of this study. Compared to the final version of the theory presented in this paper, some Table 1. Information about participants in Phase 2. Participant Experience (years) Country Ms B 25 Finland Ms C 25 Switzerland Mr D 32 Denmark Mr E 30 Belgium Ms F 10 Denmark Mr G 19 Finland 452 N. LETULĖET AL. categories had different names (e.g., “positivistic”instead of “model-based”theoretical understanding) and “client’s abilities and needs”was paired with other intervening conditions, as opposed to having its own space in the flowchart of the coding paradigm. After the presentation, the audience was informed that their input, voiced during the next 10 min, would be used as part of the study and encouraged to express any thoughts or opinions regarding the theory. Several audience members affirmed that they could relate to the theory from their own professional experience. In addition to this feedback, a suggestion was made to replace the term “intuition” with “implicit knowledge”, which was adopted. One member of the audience was approached for thorough feedback on the proposed theory. Like all the other key informants, Mr H was involved in clinical work, research, and education, but he had also had specific experience in using the grounded theory method. He was a Finnish music therapist with 26 years of experience. The meeting occurred in August 2016 and lasted 75 min. Mr H was presented with coded transcripts, figures, and tables, as well as a draft of the Abstract, Method and Results of this paper. His feedback resulted in the revision of the names of some categories and inclusion of more tables in the manuscript, as this helped to present findings more clearly. Results Results will be reported in three sections that reflect different levels of data analysis: Summary (description), Coding Paradigm (analysis) and Core Category (interpretation). Summary Informants reported the use of a variety of therapeutic methods, both active (improvisation, singing, song writing) and receptive (listening to client’s preferred music), in their clinical work. They practiced in both public and private sectors in a variety of venues (hospitals, schools, nursing homes, etc.). In response to questions they referred to examples from their work with neurological (e.g., autism, epilepsy, cerebral palsy, deafness, blindness, brain injury, intellectual disabilities) and psychopathological (e.g., depression, anxiety, bipolar disorder, schizophrenia, personality disorders, addictions) disorders and non-clinical populations (Table 2). Participants had varying degrees of familiarity with different approaches to music analysis. Some informants (Mr D, Mr E, and Ms F), had extensive knowledge of different assessment tools and had clearly defined opinions regarding the phenomenon. They Table 2. Client populations discussed in Phase 2. Participant Neurological clients Psychiatric clients Non-clinical population B × (×) C (×) × D × × (×) E×× F×× G (×) × Note. (x) –has some experience, but not the main client group. NORDIC JOURNAL OF MUSIC THERAPY 453 referred to K. Bruscia’s IAPs (Bruscia, 1987), VOIAS by S. Storm (Storm, 2013), MATADOC by W. L. Magee and colleagues (Magee, Siegert, Daveson, Lenton-Smith, &Taylor,2014), S. Malloch’s Vocal Timbre (Malloch, 1999), and E. Streeter’sworkwith Music Therapy Logbook (Streeter et al., 2012) as examples of music analysis that they did not implement in practice, but regarded highly. Others (Ms B and Ms C) tended towards implicit knowledge rather than formalised analysis methods, did not refer to other published works, and sometimes had difficulty in verbalising their implicit knowledge. Participants also had different attitudes towards music assessment in their clinical work. Ms B and Ms C liked to have more freedom –their decisions relied primarily on implicit knowledge, while the other informants employed specific methods as part of the structure of their sessions. Ms B, for example, avoided systematic work and thought that using a tool or specific method would detract from her sensitivity to an individual client. In their clinical approaches, some (Ms B & Mr D) focused on individually differing circumstances – client personalities, cultural backgrounds, and the stage of the therapeutic process –while others (Mr E & Ms F) aimed for generalizable, verifiable knowledge. Ms F said: I wanted it to be objective, the results of this type of assessment can be quite severe –if it is not good enough the child might be taken away. So my opinion should not be based on whether I like the family or not, or can relate to them –it needs to be based on objective issues. When asked about tools or specific methods of music analysis, informants’answers were grouped into one of three categories: aural, notation-based, and computational (Table 3). While all therapists –consciously or not –analysed music in an unstructured way, Ms F also used a well-documented aural music analysis method. Mr D and Mr E used traditional notation on staves. Mr D also employed graphical notation, which was based on various visual symbols. Mr E, the only user of computational software, found that notation-based analysis enabled detailed examination of music, but was time consuming, and that using computational methods was objective, fast (1 week for notation –5 min for computation), and performed complex operations, but at the current stage of development software was sometimes unreliable. Without regard to which analytical approach informants chose to use in their work, all agreed that having a variety of options was beneficial and emphasized the complementary nature of different methodologies. When answering the question “Which musical parameters do you consider the most important when analysing music?”, participants identified both primary and secondary parameters (Table 4). The most frequently discussed primary parameter was rhythmical organisation. When talking about primary parameters, informants used terms such as “cells”or “patterns”and spoke of “finding”them. Of the secondary parameters dynamics was identified most frequently during the interviews. Table 3. Phase 2 participants’use of music analysis methods. Aural Notation-based Computational B C D× E×× F× G 454 N. LETULĖET AL. while explicit knowledge can lead to frustration about interdisciplinary disagreement, greater excitement about discovery and potentially increased workloads. The explanation that this study offers for the variety of attitudes to analysing music is that each therapist needs to find a balance –professional freedom –between their inner creative impulses and professional responsibilities. On one hand, in psychodynamic music therapy clients are often encouraged to “go-with-the-flow”in a free improvisation –an activity which is believed to reflect unconscious processes taking place (De Backer & Sutton, 2014; Erkkilä, Ala-Ruona, Punkanen, & Fachner, 2012; Metzner, 2016). In order to facilitate such a flow, therapists are trained to bear uncertainty, to be spontaneous and sensitive to possible occurrence of transference or counter-transference (Hadley, 2003; Wigram, 2012). On the other hand, music therapists are expected to fill out formalised assessment tools, follow pre-existing working plans and make decisions that will affect a client’slife.As Smetana (2017) describes it, “even when structuring and directive interventions were indicated, the music therapeutic work required a high degree of flexibility and willingness for spontaneous action. Furthermore, it was always necessary to find a certain balance between presence and restraint”(p. 117). The findings of this study have implications for all disciplines that combine creative expression and health care intervention. For music therapists, it provides a means of understanding the choice of method and use of music analysis and encourages further discussion and research into it. Firstly, it appears that each music therapist –consciously or not –seeks a balance between professional responsibility and their creative impulses. It seems that this personal decision can have a wider impact on the discipline, for example, on professional recognition and the fragmentation of knowledge. Secondly, education plays an important role in therapists’attitude to music analysis (as shown by causal conditions in the coding paradigm). Based on the results of this study, we would recommend a greater emphasis on courses teaching music analysis methodology as a part of music therapists’ training programmes. In addition to formulating a grounded theory, as presented in the results, we would like to share some observations –hypotheses, if you will, to be tested in future studies. It seems that therapists who had diverse client populations and heavy workloads tended to favour context-based approaches, and that those more heavily involved in academia had a more model-based understanding. Also, it appears that participants having more musical training were more likely to analyse music. But is it that one starts to analyse music after having extensive musical training, or is it that because of having a more analytical approach to it, one seeks further training in it? Also, why did no participants mention any interpretative methods of music analysis (Trondalen & Wosch, 2016)? Did they believe these methods to be self-evident, were interview questions not inclusive enough, or maybe these methods are not commonly practised? Furthermore, no participant discussed technical limitations such as how to make a good recording or where to store data (Hadley, Hahna, Miller, & Bonaventura, 2014). Is it that having a good smartphone eliminates most of these issues today, or was this because the experienced professional participants had access to specialized facilities that are not widely available? Regardless of its exploratory nature, this study offers valuable insight into the way music therapists analyse music for assessment purposes, and also provides deeper insight into the mindset and practical approaches that expert therapists, and, by implication, music therapists as a wider community, employ. Approaches to music analysis seem to vary depending on the background, the stage of career, and the specific working environment of each individual, and this should be considered a developmental NORDIC JOURNAL OF MUSIC THERAPY 461 process, rather than a fixed choice. Despite all these interesting results, the question remains: how can we understand so differently something that is at the very core of music therapy? Maybe, after all, as Ruud (1998)wrote:“Our profession will be forever populated with people and paradigms with competing claims of knowledge. The only answer is to learn from each other and communicate what we learn”(p. 114). Acknowledgments The authors thank Duncan Snape, Elsa Campbell and Olivier Brabant for feedback on the theory. Conflict of interest No potential conflict of interest was reported by the authors. Funding This work was supported by the Jyväskylän Yliopisto [doctoral scholarship for Nerdinga Letulė]. Notes on contributors Nerdinga Letulėis a member of the Lithuanian Music Therapy Association and the Finnish Centre for Interdisciplinary Music Research. She has degrees in Musicology and Music Psychology. She is currently a music therapy doctoral candidate at the University of Jyväskylä, researching methods of music analysis that can be meaningfully applied to clinical improvisations. Email: [email protected] Esa Ala-Ruona, PhD, is a music therapist and psychotherapist working as an associate professor and senior researcher at the Music Therapy Clinic for Research and Training, University of Jyväskylä. He is the current president of The European Music Therapy Confederation. He is also a member of the Finnish Centre for Interdisciplinary Music Research, studying clinical processes in music psychotherapy, and the effects of active music therapy on post-stroke recovery. He develops clinical models of music therapy and data collection set-ups to be used with different clinical target groups. He is the co-founder of the first extensive Vibroacoustic/Physioacoustic (VAT/PA) training in Finland, and he has been developing and studying the possibilities of VAT/PA in specialized health care within psychiatry, neurology, and physiatrics. E-mail: [email protected] Jaakko Erkkilä, PhD, is professor of music therapy at University of Jyväskylä, Finland. He is the Head of the Music Therapy Clinical Trainings at the Eino Roiha Institute, in Jyväskylä and in Tampere, Finland. His clinical experience includes working with people with psychiatric and developmental disorders, and children with neurological disorders. He has been involved in research networks funded by the Academy of Finland and the European Union (EU6 and EU7 frameworks, Finnish Centre of Excellence in Interdisciplinary Music Research). He serves on the editorial boards of several music therapy journals and is a member of the Consortium of Music Therapy Research. 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A model of diagnostic assessment and analysis of musical data in music therapy. In T. Wigram (Ed.), Assessment and evaluation in the arts therapies: Art therapy, music therapy and drama therapy (pp. 77–91). Radlett, Hertfordshire, UK: Harper House. Wigram, T. (2012). Developing creative improvisation skills in music therapy: The tools for imaginative music making. In D. Hargreaves, D. Miell, & R. MacDonald (Eds.), Musical imaginations: Multidisciplinary perspectives on creativity, performance and perception (pp. 429–450). New York, NY: Oxford University Press. Appendix 1. The questionnaire used in Phase 2. (1) What are possible factors that contribute to a client’s musical expression? (a) Client’s personal qualities: musical skills, musical preferences (b) Diagnosis-related qualities (2) In your experience, how do specific disorders affect a client’s musical expression? (a) Depression –restricted expressivity (b) Developmental disorders –lack of changes (3) What are the possible ways a therapist contributes to music created together with a client? (a) Therapist’s personal qualities: personality, current mood, thoughts about the client and perhaps chemistries, unconscious factors (can be positive and negative) (b) Professional qualities: planned interventions, training, clinical experience, a specific improvisational technique/model, client’s diagnosis, knowledge of a client’s diagnosis (4) Which musical abilities do you consider meaningful in musical assessment? (a) Ability to improvise (b) Musical interaction (c) Emotional expression (5) Which musical parameters do you consider the most important when analysing music? (a) Music theory related features (e.g., melody, harmony, rhythm) (b) Basic features (e.g., pulse, dynamics, timbre) (6) Do you relate musical expression to emotional or psychological aspects? If yes, could you identify those relations? (a) High pitch might indicate joy or anxiety (b) Basic rhythm might indicate need for support (7) Do you use any tools or specific methods when analysing music? (a) What is your opinion on IAPs? (b) What is your opinion on graphical or staff notation systems? (c) What is your opinion on computational tools? (8) What are the main issues in making an assessment based on musical material? (a) It is not important in clinical work NORDIC JOURNAL OF MUSIC THERAPY 465 (b) Lack of time (c) Lack of appropriate tools (d) Lack of guidelines on how to relate results to clinical issues (9) Why are there no standardised protocols for musical assessment? (a) Clinical methods related issues (b) Clinical population related issues (c) Requirements of institutions (10) What are possible strategies that would improve the implementation of musical analysis into clinical practice? (a) Education-related (teaching methods in clinical training) (b) Research-related (develop better tools) (11) Is there something important about the assessment of musical material that we have not discussed yet? 466 N. LETULĖET AL.