Background: Building on the assertion that nearly every clinician will eventually use artificial intelligence (AI), this study provides a triangulated qualitative analysis of the requirements, challenges, and prospects for integrating AI into routine health care practice. This skills gap contributes to cautious and uneven adoption across clinical settings. Despite advancements, many health care professionals report a self-perceived lack of proficiency in comprehending, critically evaluating, and ethically deploying AI tools, which contributes to cautious adoption in clinical settings. Objective: While addressing key research questions, the study investigates the necessary prerequisites, barriers, and opportunities for AI adoption and specific training priorities that medical staff require. The study is uniquely focused on the health care workforce, moving beyond the predominant emphasis in the literature on medical students. Methods: Situated in Flanders, Belgium, a recognized innovation leader but with moderate lifelong learning participation, this research combines 15 semistructured expert interviews, a regional survey of 134 health care professionals, and 3 co-interpretive focus groups with 39 stakeholders, all conducted in 2024. Results: The results expose small generational and mainly occupational divides. For instance, 85.07% (114/134) of survey respondents expressed interest in introductory AI courses tailored to health care, while 80% (134) of them sought practical, job-relevant AI skills. However, only 13.8% (134) of clinicians felt that their training adequately prepared them for AI integration. Notably, younger professionals (50 years of age) prioritized reducing administrative burden. Physicians and dentists reported higher self-assessed AI knowledge, whereas nurses and physiotherapists showed the lowest familiarity. The survey also revealed differences in preferred learning formats, with doctors favoring flexible, asynchronous learning and nurses emphasizing the need for accredited, employer-supported training during work hours. Ethics, though emphasized in academic literature, ranked low in training interest among most practitioners, except for younger and palliative care professionals. Focus group participants confirmed the need for clear regulatory guidance and access to accredited, practically oriented training. A significant insight was that nurses often lacked institutional support and funding for training, despite their pivotal role in AI-enabled workflows. Conclusions: Taken together, these findings indicate that a one-size-fits-all approach to AI education in health care is unlikely to be effective. By triangulating insights across research stages, this study highlights the need for occupation-specific, accessible, and accredited AI training programs that bridge gaps in digital literacy and align with practical clinical priorities. The qualitative insights obtained can inform policy and training priorities in light of the European Union (EU) AI literacy mandates, while highlighting persistent gaps in workforce preparation.

The integration of artificial intelligence (AI) into qualitative research is transforming the landscape of social inquiry, raising significant epistemological and methodological, questions. This study explores the dual potential of AI to enhance the scalability in qualitative research while challenging its interpretive depth. It situates this tension within the historical trajectory of qualitative research -and specifically Grounded Theory- from positivist to constructivist paradigms, highlighting how AI’s automated, data-driven approaches may signal a resurgence of positivist assumptions. Key research questions guide this exploration: To what extent do qualitative researchers harness AI’s efficiencies in data analysis? Can the extended use of AI in qualitative research impact the depth and reflexivity essential to interpretive analysis? To delve into these questions the study employs a Technology Acceptance Model (TAM) survey combined with semi-structured interviews, strategically targeting European researchers to explore AI’s perceived usefulness, ease of use, and implications for qualitative methodologies. Survey and interview findings reveal a generational divide: early-career researchers embrace AI’s capacity for large-scale data analysis and thematic identification, while experienced researchers express scepticism about its impact on qualitative reflexivity and contextual richness. This generational gap implies that the receptiveness of younger researchers could lead to a gradual return to a methodological positivism. While this study brings a generational divide under the spotlight, future directions call for deeper investigations into the structural inequalities shaping AI adoption, such as access to resources, geography and gender.

Mainstream enterprises function by alleviating the cognitive burdens on their members and hence generating an insensitivity to all those complex environmental factors, in which they critically depend. By focusing on the political, institutional dynamics, the present article dwells on the questions of: How can social enterprises regain the embeddedness to the political, institutional environment and how can this process quest the dominant, institutional closure? To address these questions, the article introduces a synergy between new institutionalism and systems theory, both macro-sociological approaches that can compensate for each other’s deficiencies. In this context, the hybrid concept of institutional entrepreneurship describes the institutionalization of new organizational forms via the embeddedness of the institutional logics that underpin them. An in-depth qualitative research of the OTELO social enterprise and of its institutional framework of Mühlviertel was conducted. Empirical evidence shows that social entrepreneurship can regain the institutionally sustainability by becoming embedded to the fields of legitimacy, politics and discourse, and through this process dispute the broader institutional closeness.

Social innovation is conceptualised as having two intimately related pillars: institutional innovation and locally embedded innovation, in the sense of social economy. Two main research questions were addressed: how political, institutional innovation is fostered and how does it influence social economy? A mixed methods research was conducted in the Mühlviertel NUTS3 region. Despite a framework of enhanced autonomy and institutional innovation for the main stakeholders, both macro and micro analysis illustrate a lack of intermediate space to: a) link the innovative agenda to high-state political agendas, and b) link institutional innovation to social economy.