Szilvia Zörgő

Relationships between concepts are often interpreted during qualitative analysis but may remain implicit within coding structures, thematic descriptions, or analytical narratives. This article introduces the Qualitative Network Approach (QNA), a methodological framework for representing qualitative codes as interconnected elements. Its associated method, QNA network coding, specifies interpreted connections between pairs of qualitative codes according to relationship type and, where relevant, directionality. A four-step tutorial demonstrates how researchers can identify two concepts in the data for relational interpretation, identify and specify their relationship using the codebook, assign the three codes to the data fragment, and document a QNA network code. A worked interview example illustrates how individual QNA network codes can form a wider QNA network code structure. QNA may be useful when relationships between concepts are analytically important. By making relational interpretations explicit and traceable to the underlying data, QNA aims to increase the transparency and inspectability of qualitative analysis.

The Reproducible Open Coding Kit (the ROCK) is a standard for human- and machine-readable qualitative (coded) data. It was designed to be accessible to researchers without dedicated software, while simultaneously offering enough structure to enable programmatic processing. Through this combination and a number of features tailored to these ends, the ROCK lends itself well to facilitate application of open science principles in qualitative research, to catalyse methodological innovation in qualitative research, and to progress towards machine-readability and interoperability of (coded) qualitative data, to ultimately enable truly Findable, Accessible, Interoperable, and Reusable (FAIR) qualitative data. In this canonical reference, we lay out the ROCK standard v1.0. We hope this allows developers to implement the ROCK in existing free and open software, stimulates the development of additional applications, and ultimately, democratizes methodological innovation in qualitative research.

Medical ethics has relied heavily on theoretical argumentation; however, addressing complex, real-world ethical issues necessitates contextual understanding and empirical research. Qualitative methods are appropriate for exploring normative and phenomenological perspectives; such data are usually coded to explore patterns therein, which can be represented in quantitative models as well. Our objective was to showcase and address the utility of epistemic network analysis (ENA) in refining existing theory. We conducted interviews with dentists and patients on ethical challenges of patient autonomy in dentistry. Utilizing a codebook of ethical constructs based on theory, we amended the codebook with guided-inductive coding, and after coding the dataset deductively, we visualized the relative co-occurrence of codes and compared groupings of data. We refined existing ethical constructs and identified novel ones to create ENA models. Using these models, we demonstrated that examining the relative co-occurrence computed for various groupings yields richer insights into the data. However, ENA has its limitations, such as creating codes for different groups, defining those groups, or not gaining meaning from codes in isolation. Operationalizing theory and creating ENA models enables the researcher to identify points of consensus and divergence in stakeholder narratives and leverage various perspectives to inform, validate, or refine theory.

The healthcare sector, especially dentistry, contributes significantly to the environmental crisis. The behaviors and attitudes of dentists regarding sustainability practices may differ markedly; we applied the Stated and Revealed Preferences framework to compare these. Our aim was to assess a mixed method innovation, encompassing material use, digital technologies, and preventive measures in dentistry. We recruited 16 dentists based on two quotas: sex and the funding source of the clinic. Dentists’ behaviors were recorded through the structured observation of four patient sessions, while their attitudes were examined via structured interviews exploring barriers to more sustainable practices. Observation results were visualized for each dentist, depicting sustainable and unsustainable choices per session. Barriers were visualized in network graphs employing the Qualitative Network Approach. Observational data helped pinpoint (un)sustainable practices across cases and highlighted individuals whose practices diverged from the group. Qualitative networks helped identify frequent and rare associations between barriers, their types, directionality, along with themes in interview narratives. Triangulating the two data sources provided insight into case idiosyncrasies, prevalent rationales in decision-making, and apparent contradictions between intra-case behavior and attitudes. These, in turn, aided in refining data collection instruments and guiding further analyses. This novel method advances research by examining sustainable behavior on the level of individual choices, mapping the relationships between barriers, and triangulating data sources bidirectionally, thus offering the potential to inform targeted interventions that enhance sustainable practices in dentistry.

Tools that can be used to collect behavioural data during pandemics are needed to inform policy and practice. The objective of this project was to develop the Your COVID-19 Risk tool in response to the global spread of COVID-19, aiming to promote health behaviour change. We developed an online resource based on key behavioural evidence-based risk factors related to contracting and spreading COVID-19. This tool allows for assessing risk and provides instant support to protect individuals from infection. The Risk Estimation Questions assessed users’ location, age, gender, work environment, day-to-day behaviours currently performed, and conditions under which these behaviours would change. Users were also asked to estimate how often they keep their distance from others in public and regularly wash their hands, and the procedures they follow to do so. A multidisciplinary research team of more than 150 international experts developed the tool. Over 60,000 users in more than 150 countries have assessed their risk and provided data. The majority of respondents reported that they almost always keep their distance from others in public places, and most participants reported washing their hands after touching public or shared surfaces or when entering buildings. The tool, data, and results were openly shared to support government and health agencies developing behaviour change interventions. This tool creates a blueprint for similar digital infrastructure that can be replicated and used in future pandemics.

Background: The recent increase in online health information–seeking has prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user’s ability to evaluate it; yet, within the context of web-based, organic search behavior, few studies take into account both these aspects simultaneously. Objective: We aimed to explore a method to bridge these two aspects and grant even consideration to both the stimulus (web page content) and the user (ability to appraise encountered content). We examined novices and experts in information retrieval and appraisal to demonstrate a novel approach to studying information foraging theory: stimulus-engagement alignment (SEA). Methods: We sampled from experts and novices in information retrieval and assessment, asking participants to conduct a 10-minute search task with a specific information goal. We used an observational and a retrospective think-aloud protocol to collect data within the framework of an interview. Data from 3 streams (think-aloud, human-computer interaction, and screen content) were manually coded in the Reproducible Open Coding Kit standard and subsequently aligned and represented in a tabularized format with the R package {rock}. SEA scores were derived from designated code co-occurrences in specific segments of data within the stimulus data stream versus the think-aloud and human-computer interaction data streams. Results: SEA scores represented a meaningful comparison of what participants encountered and what they engaged with. Operationalizing codes as either “present” or “absent” in a particular data stream allowed us to inspect not only which credibility cues participants engaged with with the most frequency, but also whether participants noticed the absence of cues. Code co-occurrence frequencies could thus indicate case-, time-, and context-sensitive information appraisal that also takes into account the quality of information encountered. Conclusions: Using SEA allowed us to retain epistemic access to idiosyncratic manifestations of both stimuli and engagement. In addition, by using the same coding scheme and designated co-occurrences across participants, we were able to pinpoint trends within our sample and subsamples. We believe our approach offers a powerful analysis encompassing the breadth and depth of data, both on par with each other in the feat of understanding organic, web-based search behavior.

Background:The recent increase in online health information-seeking prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user’s ability to evaluate it; yet, within the context of online, organic search behavior, few studies take into account both these aspects simultaneously.Objectives:We aimed to explore a method to bridge these two aspects and grant even consideration to both the stimulus (webpage content) and the user (ability to appraise encountered content). We examined novices and experts in information retrieval and appraisal to demonstrate a novel approach to studying Information Foraging Theory: Stimulus-Engagement Alignment (SEA).Methods:We sampled from experts and novices in information retrieval and assessment, asking participants to conduct a 10-minute search task with a specific information goal. We employed an observational and a retrospective think-aloud protocol to collect data within the framework of an interview. Data from three streams (think-aloud, human-computer interaction, and screen content) were manually coded in the ROCK standard and subsequently aligned and represented in a tabularized format with the R package {rock}. SEA scores were derived from designated code co-occurrences in specific segments of data within the stimulus data stream versus the think-aloud and human-computer interaction data streams. Results:SEA scores represented a meaningful comparison of what participants encountered and what they engaged with. Operationalizing codes as either “present” or “absent” in a particular data stream allowed us to inspect not only which credibility cues participants engaged with the most frequency, but also whether participants noticed the absence of cues. Code co-occurrence frequencies could thus indicate case-, time-, and context-sensitive information appraisal that also takes into account the quality of information encountered.Discussion:Employing SEA allowed us to retain epistemic access to idiosyncratic manifestations of both stimuli and engagement, but also, via utilizing the same coding scheme and designated co-occurrences across participants, be able to pinpoint trends within our sample and subsamples. We believe our approach lends a powerful analysis encompassing the breadth and depth of data, both on par with each other in the feat of understanding organic, online search behavior.

The dominant approaches in health psychology have remained stagnant for decades (Chevance et al., 2021). Quantitative studies often employ variations on linear models, rarely questioning underlying assumptions and their implications. Qualitative studies, too, have seen little methodological progress, often applying a variation of coding the data in hierarchical code structures and describing the identified patterns. This methodological stagnation ultimately hinders scientific progress (Cartwright, 2021). This was the point of departure for a symposium we organized at the 37th Annual Conference of the European Health Psychology Society in Bremen, Germany. In that symposium, we addressed a selection of problematic practices in health psychology research and introduced a number of innovative approaches that hold the potential to transform paradigms and stimulate methodological and theoretical innovation. The aim of this article is twofold. First, to provide a brief overview of the content of our symposium. Next to the slides being publicly available (Crutzen et al., 2023), this contributes to the legacy of the symposium beyond the conference. Second, to provide those interested with more details on and links to tools in order to put these innovative approaches into practice.

Medical ethics has relied heavily on theoretical argumentation; however, addressing complex, real-world ethical issues necessitates contextual understanding and empirical research. Qualitative methods are appropriate for exploring normative and phenomenological perspectives; such data are usually coded to explore patterns therein. Our objective was to assess the utility of generating a quantitative model of code co-occurrences within qualitative data. Via an empirical study on the conflict of oral health and patient autonomy, we examined the strengths and limitations of Epistemic Network Analysis (ENA) in displaying code interactions, analyzing various groupings of data, and paying analytical heed to both the individual and groups of data providers. Despite its limitations, ENA could effectively display co-occurrence patterns in various groupings of data, which served exploratory purposes well. ENA also enabled iteration between qualitative and quantified aspects of our data, providing additional warrants for qualitative insights. Creating such models enables the researcher to identify points of consensus and divergence in stakeholder narratives and leverage various perspectives to inform, validate, or refine theory.