AimsTo explore the possible extension of the illness script theory used in medicine to the nursing context.DesignA qualitative interview study.MethodsThe study was conducted between September 2019 and March 2020. Expert nurses were asked to think aloud about 20 patient problems in nursing. A directed content analysis approach including quantitative data processing was used to analyse the transcribed data.ResultsThrough the analysis of 3912 statements, scripts were identified and a nursing script model is proposed; the medical illness script, including enabling conditions, fault and consequences, is extended with management, boundary, impact, occurrence and explicative statements. Nurses often used explicative statements when pathophysiological causes are absent or unknown. To explore the applicability of Illness script theory we analysed scripts’ richness and maturity with descriptive statistics. Expert nurses, like medical experts, had rich knowledge of consequences, explicative statements and management of familiar patient problems.ConclusionThe knowledge of expert nurses about patient problems can be described in scripts; the components of medical illness scripts are also relevant in nursing. We propose to extend the original illness script concept with management, explicative statements, boundary, impact and occurrence, to enlarge the applicability of illness scripts in the nursing domain.ImpactIllness scripts guide clinical reasoning in patient care. Insights into illness scripts of nursing experts is a necessary first step to develop goals or guidelines for student nurses’ development of clinical reasoning. It might lay the groundwork for future educational strategies.
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Climate change is one of the key societal challenges of our times, and its debate takes place across scientific disciplines and into the public realm, traversing platforms, sources, and fields of study. The analysis of such mediated debates has a strong tradition, which started in communication science and has since then been applied across a wide range of academic disciplines.So-called ‘content analysis’ provides a means to study (mass) media content in many media shapes and formats to retrieve signs of the zeitgeist, such as cultural phenomena, representation of certain groups, and the resonance of political viewpoints. In the era of big data and digital culture, in which websites and social media platforms produce massive amounts of content and network this through hyperlinks and social media buttons, content analysis needs to become adaptive to the many ways in which digital platforms and engines handle content.This book introduces Networked Content Analysis as a digital research approach, which offers ways forward for students and researchers who want to work with digital methods and tools to study online content. Besides providing a thorough theoretical framework, the book demonstrates new tools and methods for research through case studies that study the climate change debate with search engines, Twitter, and the encyclopedia project of Wikipedia.
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Content Analysis has been developed within communication science as a technique to analyze bodies of text for features or (recurring) themes, in order to identify cultural indicators, societal trends and issues. And while Content Analysis has seen a tremendous uptake across scientific disciplines, the advent of digital media has presented new challenges to the demarcation and study of content. Within Content Analysis, different strategies have been put forward to grapple with these dynamics. And although these approaches each present ways forward for the analysis of web content, they do not yet regard the vast differences between web platforms that serve content, which each have their own ‘technicities,’ e.g. carry their own (often visually undisclosed) formats and formatting, and output their own results and rankings. In this dissertation I therefore develop Networked Content Analysis as a term for such techniques of Content Analysis that are adapted specifically to the study of networked digital media content. The case in question is climate change, one of the major societal challenges of our times, which I study on the web and with search engines, on Wikipedia as well as Twitter. In all, my contribution provides footing for a return to the roots of Content Analysis and at the same time adds to its toolkit the necessary web- and platform-specific research techniques for creating a fine-grained picture of the climate change debate as it takes place across platforms.
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Social media platforms such as Facebook, YouTube, and Twitter have millions of users logging in every day, using these platforms for commu nication, entertainment, and news consumption. These platforms adopt rules that determine how users communicate and thereby limit and shape public discourse.2 Platforms need to deal with large amounts of data generated every day. For example, as of October 2021, 4.55 billion social media users were ac tive on an average number of 6.7 platforms used each month per internet user.3 As a result, platforms were compelled to develop governance models and content moderation systems to deal with harmful and undesirable content, including disinformation. In this study: • ‘Content governance’ is defined as a set of processes, procedures, and systems that determine how a given platform plans, publishes, moder ates, and curates content. • ‘Content moderation’ is the organised practice of a social media plat form of pre-screening, removing, or labelling undesirable content to reduce the damage that inappropriate content can cause.
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Over the last two decades, institutions for higher education such as universities and colleges have rapidly expanded and as a result have experienced profound changes in processes of research and organization. However, the rapid expansion and change has fuelled concerns about issues such as educators' technology professional development. Despite the educational value of emerging technologies in schools, the introduction has not yet enjoyed much success. Effective use of information and communication technologies requires a substantial change in pedagogical practice. Traditional training and learning approaches cannot cope with the rising demand on educators to make use of innovative technologies in their teaching. As a result, educational institutions as well as the public are more and more aware of the need for adequate technology professional development. The focus of this paper is to look at action research as a qualitative research methodology for studying technology professional development in HE in order to improve teaching and learning with ICTs at the tertiary level. The data discussed in this paper have been drawn from a cross institutional setting at Fontys University of Applied Sciences, The Netherlands. The data were collected and analysed according to a qualitative approach.
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PCK is seen as the transformation of content knowledge and pedagogical knowledge into a different type of knowledge that is used to develop and carry out teaching strategies. To gain more insight into the extent to which PCK is content specific, the PCK about more topics or concepts should be compared. However, researchers have rarely compared teachers’ concrete PCK about more than one topic. To examine the content dependency of PCK, we captured the PCK of sixteen experienced Dutch history teachers about two historical contexts (i.e. topics) using interviews and Content Representation questionnaires. Analysis reveals that all history teachers’ PCK about the two contexts overlaps, although the degree of overlap differs. Teachers with relatively more overlap are driven by their overarching subject related goals and less by the historical context they teach. We discuss the significance of these outcomes for the role of teaching orientation as a part of PCK.
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VHL University of Applied Sciences (VHL) is a sustainable University of AppliedSciences that trains students to be ambitious, innovative professionals andcarries out applied research to make a significant contribution to asustainable world. Together with partners from the field, they contribute to innovative and sustainable developments through research and knowledge valorisation. Their focus is on circular agriculture, water, healthy food & nutrition, soil and biodiversity – themes that are developed within research lines in the variousapplied research groups. These themes address the challenges that are part ofthe international sustainability agenda for 2030: the sustainable developmentgoals (SDGs). This booklet contains fascinating and representative examplesof projects – completed or ongoing, from home and abroad – that are linked tothe SDGs. The project results contribute not only to the SDGs but to their teaching as well.
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In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.
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One of the aims of the TALENTS-project is to create (interdisciplinary) learning communities in which engineering professionals, students, teachers, and researchers can learn together and collaborate as equal partners, within the context of authentic challenges, starting from their individual learning goals. To what extent are partners willing to participate in this partnership and under which conditions do they consider it to have added value? We conducted individual interviews with engineering students (N=11), teachers (N=12) and professionals (N=10) about what they require to participate in the learning community, employing epistemic, spatial, instrumental, temporal, and social elements of learning environments. We also inquired which resources participants were willing to invest. Data were summarized on group level in a within-group matrix, following these elements. Next, we employed a cross-group analysis, focusing on commonalities and differences. The most striking results were found in the epistemic, social, and instrumental elements. Respondents have similar needs when it comes to improving dialogue to formulate a challenge. However, professionals prefer to have more influence on formulating this challenge and its output, whereas teachers wish to focus on students’ development. Students wish to co-create with partners and they place importance on matching students with a challenge that aligns with their educational background and personal interest. To create an environment based on equality, students need traditional roles of teachers, clients, and students to be less apparent. Ultimately, almost all respondents are willing to co-operate as equal partners in the learning community because they can see it leads to added value.
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