Szilvia Zörgő 1, Gjalt-Jorn Ygram Peters 2
1Institute of Behavioural Sciences, Semmelweis University, Budapest
2Faculty of Psychology & Education Science, Open University, Netherlands
Decision-making is a topic of increasing relevance in a milieu of polycentric information production, especially with regard to questions in healthcare, such as choosing a therapy (conventional/non-conventional). The present study scrutinizes information filtering, interpretive processes, and patient behavior via a methodological innovation that enables the quantification of qualitative data. Cognitive and behavioral patterns are modelled with a novel type of network analysis.
This initiative is based on a PhD project concerning therapy choice, which took place between Jan. 2015 and June 2017, involving 105 patients (males N=42; females N=63; 53.3 mean age) through participant observation at clinics of Traditional Chinese Medicine and 20 semi-structured interviews. Within this project, an inductive code system was generated with Interpretative Phenomenological Analysis, serving as a precursory framework for the present research. Discourse and data segmentation protocols were developed to prepare data for Epistemic Network Analysis (ENA). An open source software, the Reproducible Open Coding Kit (ROCK), was created to code qualitative data and provide an interface for the “rENA” package that implements ENA.
Data collection with semi-structured interviews is in progress based on non-proportional quota sampling; strata: therapy choice (1. biomedicine, 2. complementary/alternative medicine), diagnosis, and sex. Data segmentation involved demarcating and distinguishing between meta-data and codes. Meta-data includes information on participants and group characteristics. The tentative code system consists of 3 high-level, 7 mid-level, and 53 low-level codes. Discourse segmentation was based on applying ENA terms to continuous narratives (i.e.: interview texts).
Semiotic Network Analysis aims to bridge qualitative and quantitative methods through modelling cognitive and behavioral patterns within narratives. The present project is a pilot in this methodological innovation, lending a more in-depth understanding to patient decision-making processes.
The authors would like to acknowledge the support of ÚNKP-18-3-III New National Excellence Program of the Ministry of Human Capacities.
Doctoral School: Mental Health Sciences
Program: Mental Health Sciences
Supervisor: Ágnes Zana, PhD
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