Global Journal of Human-Social Science, A: Arts and Humanities, Volume 22 Issue 7

that the undergraduates could have some guidelines, since they had never conducted open interviews, and because it is an instrument that has been used in medical courses in Brazil to facilitate the first contact of the undergraduates with narratives collected outside the traditional anamnesis scripts in the courses 16, 17 . After collecting the narratives, the participating undergraduates were welcomed by tutors in conversation circles, which were recorded and transcribed, and reflective portfolios were also prepared. Those transcripts, considering only the speeches of the students, together with the content of the reflective portfolios, composed the corpus used for this research. The corpus was submitted to lexical analysis using the free IRaMuTeQ® software (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires), created by Pierre Ratinaud in Python language, with features provided by the R statistical software 18 . It is an auxiliary software for the analysis of social representations that considers the word as a unit. Social representations, normally considered as constructions shared by and reproduced socially, help form and shape individuals, molding their role in society. Understood as common sense, they provide ways of interacting with the world, being important for the comprehension of the collective and the construction of people's personal history. They are externalized in speeches and embodied in behaviors and attitudes that guide people, even if they do not correspond to social reality 19 . For health professionals, these reproduce the culture and rationality understood as being representative of that professional training. IRaMuTeQ® generates descriptive statistics, such as the calculation of the number and frequency of words, and enables the performance of multivariate analyses, such as the Descending Hierarchical Classification (DHC). This classification works with clusters, allowing a classification in which the words are distributed in homogeneous lexical classes. In this step, the software performs chi-square tests ( χ ²), seeking the associative strength between the terms and their respective class, analyzed when the test is greater than 3.84, representing p<0.0001. From the classes, a dendrogram is generated, a graphic representation of the different clusters, which translates social representations common to the studied group 18 . In addition to the dendrogram, the software generates a phylogram of words per class, graphically translating the main terms that form them, where words located at the top of the list, highlighted by size, have greater influence in that class. It is also possible, for variables with at least three modalities (in the case of this research: course where the student is enrolled), to carry out the Correspondence Factor Analysis (CFA), a method that represents the relationships in a two- dimensional factorial plan, whose results are useful for identifying oppositions. For this study, we also used the Analysis of Specificities, or Analysis of Contrasts, which allows comparing the distribution of linguistic forms according to categorical variables, in this case, the gender mentioned by the students 18 . The use of the software, however, does not complete the analysis of the corpus , remaining the need to interpret the information generated by the researcher. This interpretation was carried out through Content Analysis 20 with the following the steps: prior analysis, exploration of material, inference and interpretation from the current theoretical-scientific framework. This analyze enables a deeper understanding of the representations that emerged from the corpus . This research was approved by the University Center Research and Ethics Committee. III. R esults Of the 22 participating undergraduates, 4 (18%) belong to the Physiotherapy course, 11 (50%) belong to the Medicine course and 7 (32%) to the Nutrition course; 14 are women (63%) and 8 (37%) are men. 13 (59%) students were in the last semesters of the course. The corpus was composed of 22 reflective portfolios and transcripts of welcoming conversation circles (Initial Context Units-ICU), material separated into 1257 text segments (Elementary Context Units-ECU), which contained 15,380 words, of which, 554 had a single occurrence. For a corpus to be considered representative of the social group studied, more than 70% of the ECUs must be used, as was the case for this research, where the use was 79.63%. The entire corpus was analyzed. However, only the most representative speeches within each class will be presented. After the DHC was performed, two subcorpora called “Health Professionals” and “Listening to Stories” emerged, the former expressing 51.1% of the ECUs and the latter 48.9%. Each of these was composed of two classes, as shown in Figure 1, which shows the dendrogram obtained. Figure 2 displays the phylogram generated from the χ 2 test for each of the classes. Only the textual elements that reached p< 0.0001 will be presented and only the excerpts with the highest score in each class will be transcribed, despite the entire corpus having been analyzed. The “Health professionals” subcorpora concentrates the largest number of text segments, thus having greater prominence. Here the students expose how they experienced the collection of illness narratives, highlighting aspects related to the recognition of the other, seen as a subject and not just a carrier of the disease, as well as the emotions that this contact brought to the surface. © 2022 Global Journals Volume XXII Issue VII Version I 3 ( ) Global Journal of Human Social Science - Year 2022 A The Use of Illness Narratives in Undergraduate Physiotherapy, Medicine, and Nutrition: Innovative Experiences at a University in São Paulo