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Framework analysis and applied qualitative research can be a perfect match, in large part because framework analysis was developed for the explicit purpose of analyzing qualitative data in applied policy research. Framework analysis is an inherently comparative form of thematic analysis which employs an organized structure of inductively- and deductively-derived themes (i.e., a framework) to conduct cross-sectional analysis using a combination of data description and abstraction. The overall objective of framework analysis is to identify, describe, and interpret key patterns within and across cases of and themes within the phenomenon of interest. This flexible and powerful method of analysis has been applied to a variety of data types and used in a range of ways in applied research. Framework analysis consists of two major components: creating an analytic framework and applying this analytic framework. This paper details the five steps in framework analysis (data familiarization, framework identification, indexing, charting, and mapping and interpretation) through conducting secondary analysis on this special issue’s common dataset. This worked example adds to the existing framework analysis methodology literature both through describing the analysis specifics and through highlighting the importance of multiple considerations of units of analysis. This paper also includes reflection on the myriad reasons that framework analysis is valuable for applied research.

Background In recent years, interventions and health policy programmes have been established to promote patient empowerment, with a particular focus on patients affected by long-term conditions. However, a clear definition of patient empowerment is lacking, making it difficult to assess effectiveness of interventions designed to promote it. The aim in this study was to develop a conceptual map of patient empowerment, including components of patient empowerment and relationships with other constructs such as health literacy, self-management and shared decision-making. Methods A mixed methods study was conducted comprising (i) a scoping literature review to identify and map the components underpinning published definitions of patient empowerment (ii) qualitative interviews with key stakeholders (patients, patient representatives, health managers and health service researchers) to further develop the conceptual map. Data were analysed using qualitative methods. A combination of thematic and framework analysis was used to integrate and map themes underpinning published definitions of patient empowerment with the views of key UK stakeholders. Results The scoping literature review identified 67 articles that included a definition of patient empowerment. A range of diverse definitions of patient empowerment was extracted. Thematic analysis identified key underpinning themes, and these themes were used to develop an initial coding framework for analysis of interview data. 19 semi-structured interviews were conducted with key stakeholders. Transcripts were analysed using the initial coding framework, and findings were used to further develop the conceptual map. The resulting conceptual map describes that patient empowerment can be conceived as a state ranging across a spectrum from low to high levels of patient empowerment, with the level of patient empowerment potentially measurable using a set of indicators. Five key components of the conceptual map were identified: underpinning ethos, moderators, interventions, indicators and outcomes. Relationships with other constructs such as health literacy, self-management and shared decision-making are illustrated in the conceptual map. Conclusion A novel conceptual map of patient empowerment grounded in published definitions of patient empowerment and qualitative interviews with UK stakeholders is described, that may be useful to healthcare providers and researchers designing, implementing and evaluating interventions to promote patient empowerment.

In this paper the author proposes a new qualitative method for building conceptual frameworks for phenomena that are linked to multidisciplinary bodies of knowledge. First, he redefines the key terms of concept, conceptual framework, and conceptual framework analysis. Concept has some components that define it. A conceptual framework is defined as a network or a “plane” of linked concepts. Conceptual framework analysis offers a procedure of theorization for building conceptual frameworks based on grounded theory method. The advantages of conceptual framework analysis are its flexibility, its capacity for modification, and its emphasis on understanding instead of prediction.

The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Our consensus process identified key contextual factors of trust, namely, an AI system's environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.

Background Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility. Results We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI. Conclusion SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available.

Background Previous research on barriers and facilitators regarding treatment-seeking of adults with depressive and anxiety disorders has been primarily conducted in the Anglosphere. This study aims to gain insight into treatment-seeking behaviour of adults with depressive and anxiety disorders in a European healthcare system. Methods In-depth semi-structured interviews were conducted with 24 participants, aged ≥18 years and diagnosed with an anxiety disorder and/or depressive disorder according to DSM-IV. Participants were purposively sampled from an outpatient department for mental health care in the Netherlands. The seven steps of framework analysis were used to identify relevant themes emerging from the interviews. Results Data analysis suggested an interplay between individual aspects, personal social system, healthcare system and sociocultural context influences. Amongst the most relevant themes were mental health illiteracy, stigma, a negative attitude toward professional help, the influence of significant others and general practitioner, and waiting time. Financial barriers were not of relevance. Conclusions Even in a country with a well-developed mental health care system and in absence of financial barriers, there are many barriers to treatment-seeking in adult patients with depressive and anxiety disorders. National campaigns to increase awareness and decrease stigma in the general population, and to empower the social environment might reduce the treatment gap.

Buildings produce a large amount of carbon emissions in their life cycle, which intensifies greenhouse-gas effects and has become a great threat to the survival of humans and other species. Although many previous studies shed light on the calculation of carbon emissions, a systematic analysis framework is still missing. Therefore, this study proposes an analysis framework of carbon emissions based on building information modeling (BIM) and life cycle assessment (LCA), which consists of four steps: (1) defining the boundary of carbon emissions in a life cycle; (2) establishing a carbon emission coefficients database for Chinese buildings and adopting Revit, GTJ2018, and Green Building Studio for inventory analysis; (3) calculating carbon emissions at each stage of the life cycle; and (4) explaining the calculation results of carbon emissions. The framework developed is validated using a case study of a hospital project, which is located in areas in Anhui, China with a hot summer and a cold winter. The results show that the reinforced concrete engineering contributes to the largest proportion of carbon emissions (around 49.64%) in the construction stage, and the HVAC (heating, ventilation, and air conditioning) generates the largest proportion (around 53.63%) in the operational stage. This study provides a practical reference for similar buildings in analogous areas and for additional insights on reducing carbon emissions in the future.

Recent models suggest that social skills training’s inefficacy for children with ADHD may be due to target misspecification, such that their social problems reflect inconsistent performance rather than knowledge/skill gaps. No study to date, however, has disentangled social skills acquisition from performance deficits in children with ADHD. Children ages 8–12 with ADHD (n = 47) and without ADHD (n = 23) were assessed using the well-validated social behavioral analysis framework to quantify cross-informant social skills acquisition deficits, performance deficits, and strengths. Results provided support for the construct and predictive validities of this Social Skills Improvement System (SSIS) alternate scoring method, including expected magnitude and valence relations with BASC-2 social skills and ADHD symptoms based on both parent and teacher report. Acquisition deficits were relatively rare and idiosyncratic for both the ADHD and Non-ADHD groups, whereas children with ADHD demonstrated cross-informant social performance deficits (d = 0.82–0.99) on several specific behaviors involving attention to peer directives, emotion regulation, and social reciprocity. Relative to themselves, children with ADHD were perceived by parents and teachers as exhibiting more social strengths than social acquisition deficits; however, they demonstrated significantly fewer social strengths than the Non-ADHD group (d = −0.71 to −0.89). These findings are consistent with recent conceptualizations suggesting that social problems in ADHD primarily reflect inconsistent performance rather than a lack of social knowledge/skills. Implications for refining social skills interventions for ADHD are discussed.

Despite providing highly accurate results, the short reads generated by second generation sequencing have major limitations in mapping complex genomic regions. Longer reads can resolve these issues and additionally phase distant variants. The third generation sequencing platform ONT currently achieves the longest sequencing reads but falls short in sequencing accuracy. Additionally, deriving phased haplotypes from amplicon-based NGS data remains a complex and time-consuming task that requires extensive bioinformatic expertise. We constructed an integrative, open-access modular data-analysis framework that allows for automated processing of high-throughput sequencing data from both second (Illumina) and third generation (ONT) sequencing platforms, combining the strengths of both technologies. Variant information is automatically evaluated and color-coded for discrepancies. Haplotypes are listed by frequency. All parts of the framework can be used independently. The framework’s performance was validated using synthetic and tested with real-life data by analyzing partly homologous FUT1/2/3 sequencing data from 400 blood donors.

The rapid spread of the SARS-CoV-2 virus has forced healthcare organizations to change their organization, introducing new ways of working, relating, communicating, and managing to cope with the growing number of hospitalized patients. Starting from the analysis of the narratives of healthcare workers who served in the intensive care units of 10 hospitals in Central and Northern Italy, this contribution intends to highlight elements present during the pandemic period within the investigated structures, which are considered factors that can influence the birth of organizational learning. Specifically, the data collected through interviews and focus groups were analyzed using the framework analysis method of Ritchie and Spencer. The conducted study made it possible to identify and highlight factors related to aspects of communication, relationships, context, and organization that positively influenced the management of the health emergency, favoring the improvement of the structure. It is believed that the identification of these factors by healthcare organizations can represent a valuable opportunity to rethink themselves, thus becoming a source of learning.