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Qualitatives Design besteht aus verschiedenen Ansätzen zur Datenerhebung und -analyse, die insbesondere für die Erstellung kultureller und kontextueller Beschreibungen und die Interpretation sozialer Phänomene anwendbar sind. Qualitative Inhaltsanalyse (QCA) und thematische Analyse (TA) stellen beides qualitative Ansätze dar, die üblicherweise von Forscher_innen aus unterschiedlichen Disziplinen gleichermaßen eingesetzt werden. Allerdings besteht in der internationalen Literatur eine Leerstelle in Bezug auf den Begriff von \"Thema\" in beiden Ansätzen und des Prozesses der Entwicklung von Themen. Daher gehen wir in unserem Beitrag auf diese Leerstelle ein und stellen Unterschiede und Ähnlichkeiten zwischen beiden Methoden in Bezug auf das \"Thema\" als Endprodukt der Datenanalyse dar. Zur Unterstützung unserer Sichtweisen und zur Erstellung international fundierter analytischer Begriffe von \"Thema\" greifen wir auf die aktuelle Literatur aus unterschiedlichen Disziplinen zurück. Wir gehen davon aus, dass Forscher_innen von einem vertieften Verständnis des Prozesses der Themenentwicklung in mehreren Hinsichten profitieren, nämlich bei der Auswahl einer geeigneten Methode für die Beantwortung ihrer Forschungsfrage, bei der Erzielung qualitativ hochwertiger und valider Ergebnisse sowie dabei, den analytischen Anforderungen an QCA und TA gerecht zu werden.

Answering recent calls for a new definition of advertising, we identify three dynamics-(new) media and formats, (new) \"consumer\" behaviors, and extended effects of advertising-that drive the evolution of advertising. Based on these, and a survey of advertising academics and professionals, we formulate an updated working definition of advertising as \"brand-initiated communication intent on impacting people.\" We also test and validate this definition and the three dynamics in a content analysis of recently published advertising research (2010 to 2015). In doing so, we hope to contribute to a more diverse and contemporary development of advertising research.

In diesem Beitrag führen wir in den ersten Teil der Schwerpunktausgabe zur qualitativen Inhaltsanalyse (QIA) ein. In einem ersten Schritt beschreiben wir die Überlegungen, die dem Schwerpunkt zugrunde liegen, und erläutern die Unterteilung in zwei Teilausgaben. Anschließend geben wir einen Überblick über zentrale Fragestellungen in der gegenwärtigen Methodenliteratur zur QIA und identifizieren vier Kernbereiche: 1. die Konzeptualisierung der QIA als hybride Kombination quantitativer und qualitativer Elemente oder als genuin qualitative Methode; 2. die Relation zwischen dem deutschsprachigen und dem internationalen Methodendiskurs; 3. die Frage, ob sich theoretische und/oder epistemologische Grundlagen der QIA identifizieren lassen; 4. fehlende Transparenz bei der Dokumentation von Anwendungen der Methode. In einem nächsten Schritt gehen wir auf den Prozess der Erstellung der Schwerpunktausgabe ein und erläutern die Struktur und die Zusammenhänge zwischen den Beiträgen. In dem vorliegenden ersten Teil legen wir den Fokus auf Artikel zur Konzeptualisierung der QIA sowie auf Beiträge, in denen Herausforderungen bei der Methodenanwendung und Lösungsansätze beschrieben werden. Auf dieser Grundlage kommen wir zu dem Schluss, dass sich in der gegenwärtigen Methodenliteratur durchaus unterschiedliche Konzeptionen der QIA identifizieren lassen. Dies spiegelt sich auch in der Vielfalt der Herausforderungen bei der Anwendung der Methode wider und in den unterschiedlichen kreativen Umgangsweisen mit diesen Herausforderungen, wie sie von den Autor_innen dieser ersten Teilausgabe beschrieben werden.

Machine and deep learning methods, today’s core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments (“big data”), and have started to find their way into medical applications. Here we will first give an overview of machine learning methods, with a focus on deep and recurrent neural networks, their relation to statistics, and the core principles behind them. We will then discuss and review directions along which (deep) neural networks can be, or already have been, applied in the context of psychiatry, and will try to delineate their future potential in this area. We will also comment on an emerging area that so far has been much less well explored: by embedding semantically interpretable computational models of brain dynamics or behavior into a statistical machine learning context, insights into dysfunction beyond mere prediction and classification may be gained. Especially this marriage of computational models with statistical inference may offer insights into neural and behavioral mechanisms that could open completely novel avenues for psychiatric treatment.

Existing models of patient-centeredness reveal a lack of conceptual clarity. This results in a heterogeneous use of the term, unclear measurement dimensions, inconsistent results regarding the effectiveness of patient-centered interventions, and finally in difficulties in implementing patient-centered care. The aim of this systematic review was to identify the different dimensions of patient-centeredness described in the literature and to propose an integrative model of patient-centeredness based on these results. Protocol driven search in five databases, combined with a comprehensive secondary search strategy. All articles that include a definition of patient-centeredness were eligible for inclusion in the review and subject to subsequent content analysis. Two researchers independently first screened titles and abstracts, then assessed full texts for eligibility. In each article the given definition of patient-centeredness was coded independently by two researchers. We discussed codes within the research team and condensed them into an integrative model of patient-centeredness. 4707 records were identified through primary and secondary search, of which 706 were retained after screening of titles and abstracts. 417 articles (59%) contained a definition of patient-centeredness and were coded. 15 dimensions of patient-centeredness were identified: essential characteristics of clinician, clinician-patient relationship, clinician-patient communication, patient as unique person, biopsychosocial perspective, patient information, patient involvement in care, involvement of family and friends, patient empowerment, physical support, emotional support, integration of medical and non-medical care, teamwork and teambuilding, access to care, coordination and continuity of care. In the resulting integrative model the dimensions were mapped onto different levels of care. The proposed integrative model of patient-centeredness allows different stakeholders to speak the same language. It provides a foundation for creating better measures and interventions. It can also be used to inform the development of clinical guidance documents and health policy directives, and through this support the shift towards patient-centered health care.

In this article | describe a series of test runs to examine the contribution that the Al-based program ChatGPT in both Versions 3.5. and 4 can make to a qualitative content analysis of interview texts. A short sample text with my sample solution is presented for this purpose. Rough inputs for a rather naive use (\"Conduct a qualitative content analysis!\") as well as differentiated specifications with questions and more precise coding instructions (prompts) led in both versions at most to rough approximations of the sample solution with a large number of gross errors. The program did not react or reacted incorrectly to different content analysis concepts (BRAUN & CLARKE, 2006; KUCKARTZ, 2014; MAYRING, 2022a; SCHREIER, 2012), did not recognize hidden text content, and failed to check for coding agreement. The results of the software, no matter what specifications were made, mostly pointed in the direction of a rough, superficial summary in the sense of a list of topics and thus appear to be less suitable for the qualitative content analysis methods | developed (MAYRING, 2022a, 2022b).

This article investigates the changes in green advertising by conceptually replicating and extending Carlson, Grove, and Kangun's 1993 study. A content analysis of 433 unique ads from 18 magazine titles published in 2009 and 2010 demonstrates that advertisers attempted to address growing public concern for the environment and the demand for green products and services as emerge from the analyses of the ads' target, objective, and the executional elements. Contrary to Carlson, Grove, and Kangun's findings, the majority of environmental claims were deemed acceptable, implying a trend toward more trustworthy and reliable green advertising.

TikTok is without any doubt the most popular social media among Gen Zers. Originally born as a lip-syncing app, it can be exploited in different ways; as such, it represents a new fertile space for political communication. In this vein, previous studies have shown that politicians all over the planet are joining the platform as a tool to connect with younger audiences. This study examines the use of TikTok in the last presidential elections in Peru. Following an affordance-based approach, we analyze all the TikToks published by the main candidates (Pedro Castillo, Keiko Fujimori, Rafael Lopez Aliaga, Hernando de Soto, and George Forsyth) during the electoral campaign, to understand if and how candidates have integrated this platform as part of their electoral strategy and what kind of content they publish and share. Through a content analysis that combines quantitative and qualitative elements, we demonstrate that, although all the analyzed politicians have opened TikTok accounts, they do not seem to take full advantage of the platform’s affordances displaying a top-down communication style with almost no deliberative nor participative intentions. Political issues are almost absent since the platform is mostly used to display their personal life and enhance their political persona, with most of the content displaying a clear entertaining dimension. Some differences are discussed but, in general, results reveal that Peruvian candidates use TikTok almost uniquely for politainment.

Media reporting of suicide has repeatedly been shown to trigger suicidal behaviour. Few studies have investigated the associations between specific media content and suicide rates. Even less is known about the possible preventive effects of suicide-related media content. To test the hypotheses that certain media content is associated with an increase in suicide, suggesting a so-called Werther effect, and that other content is associated with a decrease in suicide, conceptualised as a Papageno effect. Further, to identify classes of media articles with similar reporting profiles and to test for associations between these classes and suicide. Content analysis and latent class analysis (LCA) of 497 suicide-related print media reports published in Austria between 1 January and 30 June 2005. Ecological study to identify associations between media item content and short-term changes in suicide rates. Repetitive reporting of the same suicide and the reporting of suicide myths were positively associated with suicide rates. Coverage of individual suicidal ideation not accompanied by suicidal behaviour was negatively associated with suicide rates. The LCA yielded four classes of media reports, of which the mastery of crisis class (articles on individuals who adopted coping strategies other than suicidal behaviour in adverse circumstances) was negatively associated with suicide, whereas the expert opinion class and the epidemiological facts class were positively associated with suicide. The impact of suicide reporting may not be restricted to harmful effects; rather, coverage of positive coping in adverse circumstances, as covered in media items about suicidal ideation, may have protective effects.

This paper aims at exploring and discussing the possibilities of applying qualitative content analysis as a (text) interpretation method in case study research. First, case study research as a research strategy within qualitative social research is briefly presented. Then, a basic introduction to (qualitative) content analysis as an interpretation method for qualitative interviews and other data material is given. Finally the use of qualitative content analysis for developing case studies is examined and evaluated. The author argues in favor of both case study research as a research strategy and qualitative content analysis as a method of examination of data material and seeks to encourage the integration of qualitative content analysis into the data analysis in case study research. URN: urn:nbn:de:0114-fqs0601211