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Background: Although the use of artificial intelligence (AI)–based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults. Objective: Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC. Methods: Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area. Results: The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identifi

Objective: To describe the mental models people hold about the COVID-19 pandemic, with a focus on how they understand the factors that drive the spread of COVID-19 and what kind of beliefs are associated with these models. Design: In a series of three studies (total N = 461), we asked participants to identify factors that are relevant for COVID-19 proliferation (Study 1a), rate the importance of factors (Study 1 b), and create a mental model of how these factors relate to virus spread by employing a validated tool for mental model elicitation (Study 2). Main outcome measures: inclusion and centrality of factors in mental models of COVID-19 infection spread. Results: Mitigation measures issued by government, adherence to measures, and virus characteristics were most strongly represented in participants’ mental models. Participants who perceived measures as appropriate or who experienced more control and more worry over the spread of the virus created more complex models compared to participants who were less satisfied with measures or who felt lower control and less worry. Conclusion: These findings suggest that people are able to create sensible mental models of virus transmission but may appreciate transparent communication to comprehend the bigger picture behind the governmental mitigation strategy.

The protein transition is one of today's major societal challenges to mitigate climate change. To support lasting consumer engagement, it has been suggested to look into consumers' understanding of the protein transition to identify barriers that go beyond the practical issues of changing one's diet. The current study explored consumers' mental models of how the transition unfolds to examine which factors consumers perceive as important drivers of the transition. With a fixed set of factors and actors identified with a questionnaire, Dutch consumers (N = 214) mapped their mental models. The content and structure of the mental models were analyzed with a focus on how consumers perceive their own role. Animal well-being and environmental concerns were most often included as important drivers. The findings showed a lack of consensus about which actor(s) drive the transition (i.e., none of the actors were included by a majority of the participants). This diffusion of responsibility may be a barrier for consumers to act. Moreover, the relative simplicity of the observed mental models suggests that consumers do not yet employ systems thinking. A systems thinking mindset may help consumers understand how the system behind the transition works and how their individual contributions matter. Two avenues to encourage consumer engagement were identified: 1) emphasizing the responsibility of different actors and what consumers can contribute, and 2) encouraging a systems thinking mindset.

The primary purpose of this study was to assess the translatability of preclinical to early clinical tolerable and pharmacologically active dose ranges for central nervous system (CNS) active drugs. As a part of this, IBs were reviewed on reporting quality. Investigator's Brochures (IBs) of studies performed at the Centre for Human Drug Research (CHDR) reporting statistically significant results of CNS activity related to the drug's mechanism of action were included. The quality of IBs was assessed based on the presence of a rationale for the chosen animal model, completeness of pharmacokinetic (PK) results in reporting and internal validity information of the preclinical evidence. The IB-derisk tool was used to generate preclinical and early clinical data overviews data. For each compound, the overlap between pharmacologically active dose ranges and well-tolerated levels was calculated for three pharmacokinetic (PK) parameters: human equivalent dose (HED), maximum plasma concentration (C max) and area under the curve (AUC). Twenty-five IBs were included. In general, the quality of reporting in IBs was assessed as poor. About a third of studies did not explore the entire concentration-effect curve (pre)clinically. Single dose tolerability ranges were most accurately predicted by C max. Human equivalent dose and AUC were the best predictors of pharmacologically active ranges. Tolerable and pharmacologically active dose ranges in healthy volunteers can be reasonably well predicted from preclinical data with the IB-derisk tool. The translatability of preclinical studies can be improved by applying a higher reporting standard in IBs including comparable PK measurements across all preclinical and clinical studies.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) is widely positioned to become a key element of intelligent technologies used in the long-term care (LTC) for older adults. The increasing relevance and adoption of AI has encouraged debate over the societal and ethical implications of introducing and scaling AI. This scoping review investigates how the design and implementation of AI technologies in LTC is addressed responsibly: so-called responsible innovation (RI). RESEARCH DESIGN AND METHODS: We conducted a systematic literature search in 5 electronic databases using concepts related to LTC, AI, and RI. We then performed a descriptive and thematic analysis to map the key concepts, types of evidence, and gaps in the literature. RESULTS: After reviewing 3,339 papers, 25 papers were identified that met our inclusion criteria. From this literature, we extracted 3 overarching themes: user-oriented AI innovation; framing AI as a solution to RI issues; and context-sensitivity. Our results provide an overview of measures taken and recommendations provided to address responsible AI innovation in LTC. DISCUSSION AND IMPLICATIONS: The review underlines the importance of the context of use when addressing responsible AI innovation in LTC. However, limited empirical evidence actually details how responsible AI innovation is addressed in context. Therefore, we recommend expanding empirical studies on RI at the level of specific AI technologies and their local contexts of use. Also, we call for more specific frameworks for responsible AI innovation in LTC to flexibly guide researchers and innovators. Future frameworks should clearly distinguish between RI processes and outcomes.

Introduction Triple artemisinin-based combination therapies (TACTs) are being developed as a response to artemisinin and partner drug resistance in the treatment of falciparum malaria in Southeast Asia. In African countries, where current artemisinin-based combination therapies (ACTs) are still effective, TACTs have the potential to benefit the larger community and future patients by mitigating the risk of drug resistance. This study explores the extent to which the antimalarial drug markets in African countries are ready for a transition to TACTs. Methods A qualitative study was conducted in Nigeria and Burkina Faso and comprised in-depth interviews (n = 68) and focus group discussions (n = 11) with key actor groups in the innovation system of antimalarial therapies. Results Evidence of ACT failure in African countries and explicit support for TACTs by the World Health Organization (WHO) and international funders were perceived important determinants for the market prospects of TACTs in Nigeria and Burkina Faso. At the country level, slow regulatory and implementation procedures were identified as potential barriers towards rapid TACTs deployment. Integrating TACTs in public sector distribution channels was considered relatively straightforward. More challenges were expected for integrating TACTs in private sector distribution channels, which are characterized by patient demand and profit motives. Finally, several affordability and acceptability issues were raised for which ACTs were suggested as a benchmark. Conclusion The market prospects of TACTs in Nigeria and Burkina Faso will depend on the demonstration of the added value of TACTs over ACTs, their advocacy by the WHO, the inclusion of TACTs in financial and regulatory arrangements, and their alignment with current distribution and deployment practices. Further clinical, health-economic and feasibility studies are required to inform decision makers about the broader implications of a transition to TACTs in Afr

National and international laws and regulations exist to protect animals used for scientific purposes in translational and applied research, which includes drug development. However, multiple animal models are available for each disease. We evaluated the argumentation behind the selection of a specific animal model using thematic content analysis in project applications issued in 2017-2019 in the Netherlands. In total, 125 animal models for translational and applied research from 110 project applications were assessed. Explanations to select a specific model included: the model’s availability (79%); the availability of expertise (62%); and the model showing similar disease pathology/symptoms (59%) to humans. Therefore, current selection of a specific animal model seems to be based on tradition rather than its potential predictive value for clinical outcome. The applicants’ explanations for the implementation of the 3R prin­ciples (replacement, reduction and refinement) as to the animal model were unspecific. Replacement was achieved by using data from prior in vitro studies, reduction by optimal experimental design and statistics, and refinement by reducing discomfort. Additionally, due to the stated need for a test model with high complexity (47%) and intactness (30%), the full replacement of animal models with alternative (non-live animal) approaches was thought unachievable. Without a clear, systematic and transparent justification for the selection of a specific animal model, the likelihood of poorly trans­latable research remains. It is not only up to the researcher to demonstrate this, as ethical committees and funding bodies can provide positive stimuli to drive this change.