Anna Jeney

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.

Introduction Lack of skills, commitment, and poor health behavior are barriers to physicians providing lifestyle counseling. We aimed to explore the role of doing school-based peer health education during training in overcoming these barriers. We studied educators of the Balassagyarmat Health Education Program (BEP) to investigate what experiences they had gained that are useful for medical practice, how committed they had become to health education, and whether their health behavior had changed. Methods Semi-structured interviews were conducted with those medical student peer educators of BEP (n=9) who had mastered all topics of the program. Our data were subjected to qualitative content analysis. Our deductive codes were defined by the research questions, and responses to these were inductively coded and aggregated along the questions. Results Participants cited improvements in patient education, motivation, and partnership building as useful for the practice. Their commitment strengthened and they voluntarily and successfully conducted health education in their communities and with their patients following the program. They reported changes in their behavior in terms of more conscious alcohol use and diet and increased physical activity. Conclusions The health education activity helped peer educators develop medical skills and commitment to health education and positive health behavior change; thus, we recommend its integration into medical education.

Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking. In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples. This paper describes our methods and planned analyses elaborated with mock figures.

When employing unified, quantitative-qualitative methods such as Epistemic Network Analysis (ENA), the relative frequency of codes and their co-occurrence is of interest. However, in projects utilizing a large number of codes, the interpretation of these models becomes challenging. In this paper, we provide three potential approaches to code selection. In the theory-based approach, code clustering and selection is founded on relevant literature or theory. In the insight-based approach, clusters of codes are defined by the grounded observations of researchers. Lastly, in the model-based approach, fully inclusive ENA models are generated to select codes for future models. We illustrated these approaches using data from our ongoing project that aims to measure the effects of a health education intervention, the Balassagyarmat Health Education Program, on near-peer educators’ understanding regarding the biopsychosocial model of health. All three approaches may be useful in guiding code selection for final ENA models or in providing a baseline for further refinement of model parameters. By outlining these approaches, this work contributes to discourse on making conscious and transparent decisions regarding ENA parameterization.

Patient decision-making concerning therapy choice has been thoroughly investigated in the Push/Pull framework: factors pushing the patient away from biomedicine and those pulling them towards Complementary and Alternative Medicine (CAM). Others have examined lay etiology as a potential factor in CAM use. We conducted semi-structured interviews with patients employing only biomedicine and those using CAM. The coded and segmented data was quantified and modelled using epistemic network analysis (ENA) to explore what effects push/pull factors and etiology had on the decision-making processes.There was a marked difference between our two subsamples concerning push factors: although both groups exhibited similar scaled relative code frequencies, the CAM network models were more interconnected, indicating that CAM users expressed dissatisfaction with a wider array of phenomena. Among pull factors, a preference for natural therapies accounted for differences between groups but did not retain a strong connection to rejecting conventional treatments. Etiology, particularly adherence to vitalism, was also a critical factor in both choice of therapy and rejection of biomedical treatments. Push factors had a crucial influence on decision-making, not as individual entities, but as a constellation of experienced phenomena. Belief in vitalism affects the patient’s explanatory model of illness, changing the interpretation of other etiological factors and illness itself. Scrutinizing individual push/pull factors or etiology does not explain therapeutic choices; it is from their interplay that decisions arise. Our unified, qualitative-andquantitative methodological approach offers novel insight into decision-making by displaying connections among codes within patient narratives.

Diabetes is one of the most prevalent chronic diseases and necessitates ongoing self-management to control symptoms. People with diabetes (PWD) express various needs, which are often met in online support groups. Our objectives were to examine related discourse in Hungarian Facebook groups in order to map the content and structure of information PWD are sharing. We aimed to juxtapose our findings with the functionality of eHealth tools available to Hungarian patients to understand what these tools may be lacking. We extracted a total of 200 threads from two public self-help groups. Codes were developed inductively and applied to the entire corpus with the Interface for the Reproducible Open Coding Kit. We used Epistemic Network Analysis to model our data. The mean network of the entire dataset indicates that discourse was chiefly centered on providing help regarding lifestyle-related issues, especially diet. Critical facets of discourse included the need by experienced PWD to provide guidance to the newly diagnosed, share "street-level" knowledge on diabetes, and ensure a sense of support and community. Existing eHealth tools could be supplemented with an app that automatically downloads blood glucose meter measurements, warns patients about predicted high glucose levels, enables location-based information exchange, and promotes social support in adhering to diet and exercise. Ideally, this app would connect patients with their physicians and/or dietitians.