A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Empirical Analysis of the Constitution of the United States of America Through Organizational Charts, Narrative Content and Network Analysis

iScale will typically result in a wealth of experience narratives relating to different stages of products’ adoption. The qualitative analysis of these narrative is a labor intensive, and prone to researcher bias activity. This chapter proposes a semi-automated technique that aims at supporting the researcher in the content analysis of experience narratives. The technique combines traditional qualitative coding procedures (Strauss and Corbin, 1998) with computational approaches for assessing the semantic similarity between documents (Salton et al., 1975). This results in an iterative process of qualitative coding and visualization of insights which enables to move quickly between high-level generalized knowledge and concrete and idiosyncratic insights. The proposed approach was compared against a traditional vector-space approach for assessing the semantic similarity between documents, the Latent-Semantic Analysis (LSA), using a dataset of a study in chapter 4. Overall, the proposed approach was shown to perform substantially better than traditional LSA. However, interestingly enough, this was mainly rooted in the explicit modeling of relations between concepts and individual terms, and not in the restriction of the list of terms to the ones that concern particular phenomena of interest.KeywordsContent AnalysisSemantic SimilarityLatent Semantic AnalysisLatent ConceptAutomate ApproachThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Drawing on theoretical and methodological papers written over the past thirty years, this volume provides an introduction to narrative inquiry. It provides an overview of the development of narrative inquiry within the social sciences and the humanities, and establishes its relevance for social work and related professions. It presents one research design, the case study design, in which narrative inquiry may be placed. It provides a broad framework in which narrative and other relevant data may be collected and specifies ways in which audio-recordings of interviews may be transcribed. It presents major approaches to the analysis of transcribed interviews. These approaches include ones that focus on the analysis of narrative content, narrative structure, and narrative in context. The volume also examines two emerging approaches to narrative analysis, critical narrative analysis and contextual discursive analysis. Each narrative analytic approach covered is considered in relation to the same set of dimensions including definition of narrative, theoretical orientation, central question, and method. Exemplary investigations that rely on each approach are included. Issues pertaining to reflexivity, ethics, and validity of narrative inquiry are explored. The appendices contain an outline for a narrative research proposal.

The purpose of this paper is the explanation of the dynamics of innovation activity in Russia (on the example of the level of innovation activity of organizations) and identification of the problems for innovative development, based on the methodology of narrative institutional analysis. Research methods are narrative and content analysis, a graphical method for displaying statistical data. The study formulates the differences in the interpretation and methods of analysis of narratives between the narrative economy (in the tradition of R. Schiller) and the narrative analysis in other social sciences. The application of the approach of narrative economics to the study of the Russian innovation system provides a study of narratives that combines the qualitative and quantitative research methods. Narratives in R. Schiller’s approach are interpreted as stories or expressed explanations of events, discussed in conversations, news, and social media to stimulate interest or demonstrate emotions. The problem of interrelation between the narratives about the innovation activity and the indicators of the innovation activity is considered. The distinction between the direct and indirect influence of narratives on indicators of innovative activity is proposed. The direct influence of narratives on the indicators of innovation activity may include modifying the methods for calculating indicators. The indirect influence of narratives on the indicators of innovation activity may include modifying the motivation and level of activity of the actors of the national innovation system. The observed dynamics of indicators of innovative activity indicate its stagnation, at the same time, the volume of discussion in the media about innovations is increasing. The paper discusses possible reasons that impede the strengthening of innovative development. The main focus is on assessing the importance of the problem of business demand for innovations and commercialization of innovations. The paper formulates a theoretical framework that summarizes the types of problems that may arise during the implementation of innovative activities. There are five types of problems that may arise during the implementation of innovative activities. The first type of the problems is caused by government compulsion, institutional constraints and intervention in economic activity. The second type of problem is caused by the opportunism of individuals who are selfish at any cost and are capable for violating rules and agreements. The third type of problem is associated with the difficulty of coordination and adapting the plans of free individuals, each of whom has only a part of the common knowledge that should be used in economic activity. The fourth type of problems is the lack of resources and knowledge for innovation; it is well studied by economic science. The fifth type of problems is the inability of individuals to make the optimal choice; in relation to the Russian innovation system, this is only a hypothesis and has not yet been studied. The first three «ideal types» of problems are incorporated in the narrative of the low demand for innovation from the business side.

Abstract. To Electronic health records (EHRs) are a treasure trove of patient information, including clinical notes. However, extracting meaningful insights from these unstructured narratives remains a challenge. This paper explores the potential of natural language processing (NLP) and deep learning to unlock the rich clinical narrative data and revolutionize healthcare. Clinical notes capture a wealth of information beyond structured data points. They document patient history, symptoms, treatment progress, and physician observations. Analyzing this narrative unlocks a deeper understanding of a patient's health journey, aiding in NLP can identify subtle patterns in language usage that might be indicative of specific diseases. This can assist clinicians in arriving at more accurate diagnoses, particularly for complex cases. Deep learning models can analyze narratives to identify patients at higher risk of developing certain conditions. This allows for proactive interventions and preventative measures. Extracting details about a patient's lifestyle, social context, and emotional well-being from narratives can facilitate the development of personalized treatment plans that cater to individual needs. NLP techniques like named entity recognition, sentiment analysis, and topic modeling enable the extraction of key clinical entities, emotions, and themes from narratives. Deep learning, with its ability to learn complex relationships in text data, empowers NLP tasks. Deep learning models can be trained on massive datasets of clinical notes, achieving superior performance in tasks like information extraction compared to traditional NLP methods. Deep learning models can handle the vast amount of text data generated in healthcare settings, enabling large- scale analysis of clinical narratives. By learning from diverse patient populations, deep learning models can improve their generalizability and adapt to variations in language usage within clinical documentation. Integrating NLP and deep learning into clinical workflows promises a future where the patient narrative is not just documented but actively analyzed to inform better healthcare decisions. This can lead to earlier diagnoses, more effective treatments, and ultimately, improved patient outcomes. By addressing the challenges and fostering responsible development, we can unlock the true potential of clinical narratives and empower a new era of data-driven healthcare.

To review studies of Natural Language Processing (NLP) systems that assist in cancer care, explore use cases and summarise current research progress. A scoping review, searching six databases (1) MEDLINE, (2) Embase, (3) IEEE Xplore, (4) ACM Digital Library, (5) Web of Science, and (6) ACL Anthology. Studies were included that reported NLP systems that had been used to improve cancer management by patients or clinicians. Studies were synthesised descriptively and using content analysis. Twenty-nine studies were included. Studies mainly applied NLP in mixed cancer types (n=10, 34.48%) and breast cancer (n=8, 27.59%). NLP was used in four main ways: (1) to support patient education and self-management; (2) to improve efficiency in clinical care by summarising, extracting, and categorising data, and supporting record-keeping; (3) to support prevention and early detection of patient problems or cancer recurrence; and (4) to improve cancer treatment by supporting clinicians to make evidence-based treatment decisions. Studies highlighted a wide variety of use cases for NLP technologies in cancer care. However, few technologies have been evaluated within clinical settings, none have been evaluated against clinical outcomes, and none have been implemented into clinical care. NLP has the potential to improve cancer care via several mechanisms, including information extraction and classification, which could enable automation and personalization of care processes. Additionally, NLP tools such as chatbots show promise in improving patient communication and support. However, there are deficiencies in the evaluation and clinical integration challenges. Interdisciplinary collaboration between computer scientists and clinicians will be essential if NLP technologies are to fulfil their potential to improve patient experience and outcomes. Registered Protocol: https://doi.org/10.17605/OSF.IO/G9DSR.

Qualitative descriptive research can be used when researchers are seeking to find the "how," "what," or "when" of phenomena. The most common qualitative descriptive analysis methods are content and thematic analyses. Data triangulation through content analysis and natural language processing was first described in 2018 for the analysis of nurse-to-nurse communication in an acute care setting. The purpose of this article is to discuss a within-methods data triangulation of interviews done with nurses and nursing leaders in Magnet- and non-Magnet-designated hospitals through integration and application of content analysis, code quantification via the Goodwin statistic, and natural language processing.

The CoViD-19 pandemic generated huge quantities of healthcare and clinical data. Artificial intelligence (AI) may help in processing such data, to support patient care, and to plan healthcare actions for pandemic control. This paper aims at analyzing the state of the art of AI and Clinical Information Systems to support the management of CoViD-19 patients. The analysis is performed according to a proposed taxonomy based on methodologies and techniques, and it also discusses some research directions.We consider and extend some recent taxonomies, for classifying intelligent information systems and Artificial Intelligence techniques for data-intensive applications. We consider methodologies and techniques for CoViD-19 pandemic data analysis.We describe the state of the art, according to the proposed taxonomy, i.e., data collection, machine learning, natural language processing, process mining and pathway identification, decision support systems.We analyze and highlight some emerging directions for short- and mid-term research activities.

Research on lifestyle changes during the COVID-19 pandemic often relies on Likert-type scale question surveys. Survey participants respond to questions by selecting one of the numerically ordered choices, "Strongly Disagree"=1, "Disagree"=2, "Neutral"=3, "Agree"=4, and "Strongly Agree"=5. Analyzing Likert-type data requires statistical methods beyond approaches like linear regression. First, it is unclear if the distance between choices are truly equal. For example, are "Agree" and "Strongly Agree" more close than "Neutral" and "Agree? Second, summarizing results using traditional means makes little sense. For example, would a mean of 4.5 imply "Agree and a half"? Finally, participants tend to select more central choices and less extremes.

This research aims to identify possible approaches for achieving hearing diversity by harmonizing a melody composed according to the rules of Turkish music using various methods. The study also aims to conduct an original application based on these approaches, obtain specialist opinions on the samples within the application, and examine the resulting situations to contribute to relevant future studies. The research is based on a mixed model, incorporating both a descriptive field study and design and development research. Qualitative data collection tools were used to obtain descriptive data. Data collected through the literature review method were processed on the study piece to determine the fundamental content of the study and present theoretical information within the framework of specified harmonization approaches. The Turkish folk song Yörük Ali, the study piece of the research, was selected using a purposive sampling approach from non-random sampling methods. The application was conducted on the study piece selected within the context of the Hüseyni makam, with three vertical context examples: triadic harmony system, quartal harmony system, and mixed harmony system. A semi-structured interview form was created for the questions directed to specialists, data were collected through interviews, content analysis was performed, and the results were tabulated and interpreted. All the specialists whose opinions were taken stated that they found the piece, degrees, chords, chords connections within the samples appropriate. The general average preference ranking of the specialists was: triadic harmony system, mixed harmony system, and quartal harmony system. Based on the results and evaluations, it is concluded that each sample applied to the study piece provides hearing diversity and that all these approaches can be considered and applied in terms of various technical and aesthetic concerns for melodies written in the Hüseyni makam and studies to be conducted on this makam scales. It is believed that increasing and disseminating studies and experiments in this field, organizing conferences, symposiums, and workshops, and establishing the rules of the polyphonic dimension of Turkish music within a systematic framework will contribute to developing this area.

Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance tools for various legal processes, has captured the interest of researchers for years. This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 154 studies, with a final selection of 131 after manual filtering. It explores foundational concepts related to NLP in the legal domain, illustrating the unique aspects and challenges of processing legal texts, such as extensive document lengths, complex language, and limited open legal datasets. We provide an overview of NLP tasks specific to legal text, such as Document Summarisation, Named Entity Recognition, Question Answering, Argument Mining, Text Classification, and Judgment Prediction. Furthermore, we analyse both developed legal-oriented language models, and approaches for adapting general-purpose language models to the legal domain. Additionally, we identify sixteen open research challenges, including the detection and mitigation of bias in artificial intelligence applications, the need for more robust and interpretable models, and improving explainability to handle the complexities of legal language and reasoning.

1. Locating Narrative Inquiry in the Interdisciplinary Context Scientific Research and Qualitative Research in Tandem Narrative Inquiry Narrative Inquiry in Different Disciplines Some Cautionary Tales About Narrative Inquiry Narrative Inquiry as the Synergy of Interdisciplinarity Learning to be a Storyteller in the Interdisciplinary Context Conclusion: A Falling Apple 2. Philosophical/Theoretical Underpinnings of Narrative Inquiry The Role of Theory Philosophical/Theoretical/Interpretive Paradigm Theory and Narrative Inquiry Critical Theory Critical Race Theory Feminist Theory Phenomenology Poststructuralism/Postmodernism/Deconstruction Dewey's Theory of Experience Bakhtin's Theory of Novelness Conclusion: No Theory Used as a Procrustean Bed 3. Narrative Research Design: Engaging in Aesthetic Play Engaging in Aesthetic Play Standards for Humanities-Oriented Research Learning to Think Narratively Plowing Before Sowing: On Reviewing the Literature Developing Good Research Questions Imagining the Researcher-Participant Relationship: From Spy to Friend Ethical Issues in Narrative Inquiry Qualitative Writing vs. Scientific Writing Imagining Narrative Writing as Aesthetic Play Conclusion: Where Your Heart Belongs 4. Narrative Research Genres: Meditating Stories Into Being Narrative Inquirer as a Midwife Narrative Research Genres Autobiographical Narrative Inquiry Biographical Narrative Inquiry Arts-Based Narrative Inquiry Literary-Based Narrative Inquiry Visual-Based Narrative Inquiry Conclusion: Blurring Genres 5. Narrative Data Collection Methods: Excavating Stories Narrative Thinking Interview Logistics Types of Qualitative Interview Narrative Interviewing Fieldwork Artifacts: Cabinets of Curiosities or Cabinets of Wonder Visual Data Digital Archival Data Conclusion: Excavating Stories as Data 6. Narrative Data Analysis and Interpretation: Flirting With Data On Flirtation Qualitative Data Analysis Theorizing Narrative Data Analysis and Interpretation Methods of Narrative Data Analysis Narrative Analysis in Narrative Genres Conclusion: Variegations of Narrative Analysis and Interpretation 7. Narrative Coda: Theorizing Narrative Meaning On Coda Researching Signature Answering the Question So What? Desiring and Audience Avoiding an Epic Closure Theorizing Findings Planting the Seed for Social Justice Becoming a Scheherazade Conclusion: Ongoing Stories 8. Critical Issues in Narrative Inquiry: Looking Into a Kaleidoscope Looking into a Kaleidoscope Contradicting Stories: The Rashomon Effect Temptation of Backyard Research On Reflexivity On Bricolage and Bricoleur On Small Stories Storytelling as Performance Conclusion: Pushing the Boundary of Narrative Inquiry 9. Examples of Narrative Inquiry: Theory Into Practice Narrative Inquiry as Phenomenon and Method Narrative Inquiry as Oral History Narrative Inquiry as Life Story Narrative Inquiry as Autoethnography Narrative Inquiry as Creative Fiction Narrative Inquiry as Fiction Conclusion: Learning to be a Storyteller 10. Epilogue

This study conducts a systematic review of literature on online health consultation (OHC) from 2 major databases, and then iteratively identifies themes in the literature using a content analysis process. A total of 129 studies met the inclusion criteria. The reviewed articles are divided into four themes according to an integrated classic doctor-patient communication model. The most commonly concerned theme in OHC research is Process (n=60, 47%), which includes Instrumental Behavior (n=22), Affective Behavior (n=22), and other behaviors (n=16). The second concerned theme is Outcome (n=35, 27%). Within this theme, Patient Outcome (n=26) gains much more attention than Doctor Outcome (n=9). The remaining themes are External Structure (n=16, 12%) and Internal Structure (n=18, 14%). Finally, a systematic framework is proposed to illustrate the current state of the research and guide future research.

This study extends peace journalism (PJ) to audiovisual reporting by analyzing 405 TikTok videos from six news outlets during the Israel-Gaza War that began on October 7, 2023. A content analysis revealed a strong emphasis on elite-captured perspectives that often eclipsed narratives of peace. TikTok users engaged more with war narratives due to their emotionally charged and visually resonant nature. Our findings highlight the need for news organizations to leverage TikTok’s visual storytelling potential to make complex geopolitical content more engaging, fostering empathetic and inclusive dialogue regarding conflicts. Overall, this work offers significant theoretical and practical contributions to peace journalism, platform affordances, and user engagement.