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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Epidemiological oral health data are essential for informing public health policy and evaluating care. In the Netherlands, OrangeForce and the Oral Health Monitor seek to resume data collection by extracting information from dental practices using both automated (ie, software-based extraction) and manual (ie, user-entered in web-based forms) methods. As the success of these methods depends on cooperation from providers, this study aimed to explore the perspectives of Dutch dentists on collecting oral health data from practices, focussing on automated and manual data collection.
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In this paper we present the first freely available corpus of Dutch text messages containing data originating from the Netherlands and Flanders. This corpus has been collected in the framework of the SoNaR project and constitutes a viable part of this 500-million-word corpus. About 53,000 text messages were collected on a large scale, based on voluntary donations. These messages will be distributed as such. In this paper we focus on the data collection processes involved and after studying the effect of media coverage we show that especially free publicity in newspapers and on social media networks results in more contributions. All SMS are provided with metadata information. Looking at the composition of the corpus, it becomes visible that a small number of people have contributed a large amount of data, in total 272 people have contributed to the corpus during three months. The number of women contributing to the corpus is larger than the number of men, but male contributors submitted larger amounts of data. This corpus will be of paramount importance for sociolinguistic research and normalisation studies.
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Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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Like a marker pen on a map, the Covid-19 pandemic drastically highlighted the persisting existence of borders that used to play an ever decreasing role in people´s perception and behavior over the last decades. Yes, inner European borders are open in normal times. Yes, people, goods, services and ideas are crossing the border between Germany and the Netherlands freely. Yet we see that the border can turn into a barrier again quickly and effectively and it does so in many dimensions, some of them being not easily visible. Barriers hinder growth, development and exchange and in spite of our progress in creating a borderless Europe, borders still create barriers in many domains. Differing labor law, social security and tax systems, heterogeneous education models, small and big cultural differences, language barriers and more can impose severe limitations on people and businesses as they cross the border to travel, shop, work, hire, produce, buy, sell, study and research. Borders are of all times and will therefore always exist. But as they did so for a long time, huge opportunities can be found in overcoming the barriers they create. The border must not necessarily be a dividing line between two systems. It has the potential to become a center of growth and progress that build on joint efforts, cross-border cooperation, mutual learning and healthy competition. Developing this inherent potential of border regions asks for politics, businesses and research & education on both sides of the border to work together. The research group Cross-Border Business Development at Fontys University of Applied Science in Venlo conducts applied research on the impact of the national border on people and businesses in the Dutch-German border area. Students, employees, border commuters, entrepreneurs and employers all face opportunities as well as challenges due to the border. In collaboration with these stakeholders, the research chair aims to create knowledge and provide solutions towards a Dutch-German labor market, an innovative Dutch-German borderland and a futureproof Cross-Border economic ecosystem. This collection is not about the borderland in times of COVID-19. Giving meaning to the borderland is an ongoing process that started long before the pandemic and will continue far beyond. The links that have been established across the border and those that will in the future are multifaceted and so are the topics in this collection. Vincent Pijnenburg outlines a broader and introductory perspective on the dynamics in the Dutch-German borderland.. Carla Arts observes shopping behavior of cross-border consumers in the Euregion Rhine-Meuse-North. Jan Lucas explores the interdependencies of the Dutch and German economies. Jean Louis Steevensz presents a cross-border co-creation servitization project between a Dutch supplier and a German customer. Vincent Pijnenburg and Patrick Szillat analyze the exitence of clusters in the Dutch-German borderland. Christina Masch and Janina Ulrich provide research on students job search preferences with a focus on the cross-border labor market. Sonja Floto-Stammen and Natalia Naranjo-Guevara contribute a study of the market for insect-based food in Germany and the Netherlands. Niklas Meisel investigates the differences in the German and Dutch response to the Covid-19 crisis. Finally, Tolga Yildiz and Patrick Szillat show differences in product-orientation and customer-orientation between Dutch and German small and medium sized companies. This collection shows how rich and different the links across the border are and how manifold the perspectives and fields for a cross-border approach to regional development can be. This publication is as well an invitation. Grasping the opportunities that the border location entails requires cooperation across professional fields and scientific disciplines, between politics, business and researchers. It needs the contact with and the contribution of the people in the region. So do what we strive for with our cross-border research agenda: connect!
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In this project we take a look at the laws and regulations surrounding data collection using sensors in assistive technology and the literature on concerns of people about this technology. We also look into the Smart Teddy device and how it operates. An analysis required by the General Data Protection Regulation (GDPR) [5] will reveal the risks in terms of privacy and security in this project and how to mitigate them. https://nl.linkedin.com/in/haniers
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The Technical Manual for the digital evaluation tool QualiTePE supports users of the QualiTePE tool in creating, conducting and analysing evaluations to record the quality of teaching in physical education. The information on the General Data Protection Regulation (GDPR) instructs users on how to anonymise the data collection of evaluations and which legal bases apply with regard to the collection of personal data. The technical manual for the digital evaluation tool QualiTePE and the information on the General Data Protection Regulation (GDPR) are available in English, German, French, Italian, Spanish, Dutch, Swedish, Slovenian, Czech and Greek.
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Locative mapping with smartphones is the process of collecting geo data using mobile applications that tells something about a place, based on user input. Results are typically visualized using maps. Usually when such data is collected, the researcher is interested in more than just the location of a person. Perhaps the researcher would like to ask some questions, add a photo or make a voice recording. Questions such as what types of data you want to collect and with whom greatly influence how to design this data collection process. In this HowTo we aim to guide you through such a process and help you make the best decision what software is best for your project. We further elaborate on how such a data collection process can be paired with participatory methods to get a deeper level of understanding of the data collected.
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The Sport Empowers Disabled Youth 2 (SEDY2) project encourages inclusion and equal opportunities in sport for youth with a disability by raising their sports and exercise participation in inclusive settings. The SEDY2 Collection of Inclusion Best Practices report contains good examples of inclusion on youth with a disability in sport at the community and institutional level. This report includes a detailed description of the process of building and using the SEDY2 approach for collection international best practices in sport, the criteria and template used to collect the SEDY2 best practices and the list of SEDY2 international best practices on inclusion in sport for youth with a disability.
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Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
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