From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.
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Objective To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors. Design A prospective cohort study with a 6-month follow-up between January 2020 and March 2023. Setting 30 physiotherapy primary care practices. Participants Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset. Baseline measures Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude. Outcome measures Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain. Results 62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer–Lemeshow test yielded a p-value of 0.7167, indicating a good model fit. Conclusion This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.
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Design and development practitioners such as those in game development often have difficulty comprehending and adhering to the European General Data Protection Regulation (GDPR), especially when designing in a private sensitive way. Inadequate understanding of how to apply the GDPR in the game development process can lead to one of two consequences: 1. inadvertently violating the GDPR with sizeable fines as potential penalties; or 2. avoiding the use of user data entirely. In this paper, we present our work on designing and evaluating the “GDPR Pitstop tool”, a gamified questionnaire developed to empower game developers and designers to increase legal awareness of GDPR laws in a relatable and accessible manner. The GDPR Pitstop tool was developed with a user-centered approach and in close contact with stakeholders, including practitioners from game development, legal experts and communication and design experts. Three design choices worked for this target group: 1. Careful crafting of the language of the questions; 2. a flexible structure; and 3. a playful design. By combining these three elements into the GDPR Pitstop tool, GDPR awareness within the gaming industry can be improved upon and game developers and designers can be empowered to use user data in a GDPR compliant manner. Additionally, this approach can be scaled to confront other tricky issues faced by design professionals such as privacy by design.
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For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.
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Background: Peer review is at the heart of the scientific process. With the advent of digitisation, journals started to offer electronic articles or publishing online only. A new philosophy regarding the peer review process found its way into academia: the open peer review. Open peer review as practiced by BioMed Central (BMC) is a type of peer review where the names of authors and reviewers are disclosed and reviewer comments are published alongside the article. A number of articles have been published to assess peer reviews using quantitative research. However, no studies exist that used qualitative methods to analyse the content of reviewers’ comments. Methods: A focused mapping review and synthesis (FMRS) was undertaken of manuscripts reporting qualitative research submitted to BMC open access journals from 1 January – 31 March 2018. Free-text reviewer comments were extracted from peer review reports using a 77-item classification system organised according to three key dimensions that represented common themes and sub-themes. A two stage analysis process was employed. First, frequency counts were undertaken that allowed revealing patterns across themes/sub-themes. Second, thematic analysis was conducted on selected themes of the narrative portion of reviewer reports. Results: A total of 107 manuscripts submitted to nine open-access journals were included in the FMRS. The frequency analysis revealed that among the 30 most frequently employed themes “writing criteria” (dimension II) is the top ranking theme, followed by comments in relation to the “methods” (dimension I). Besides that, some results suggest an underlying quantitative mindset of reviewers. Results are compared and contrasted in relation to established reporting guidelines for qualitative research to inform reviewers and authors of frequent feedback offered to enhance the quality of manuscripts. Conclusions: This FMRS has highlighted some important issues that hold lessons for authors, reviewers and editors. We suggest modifying the current reporting guidelines by including a further item called “Degree of data transformation” to prompt authors and reviewers to make a judgment about the appropriateness of the degree of data transformation in relation to the chosen analysis method. Besides, we suggest that completion of a reporting checklist on submission becomes a requirement.
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This study focuses on SME networks of design and high-tech companies in Southeast Netherland. By highlighting the personal networks of members across design and high-tech industries, the study attempts to identify the main brokers in this dynamic environment. In addition, we investigate whether specific characteristics are associated with these brokers. The main contribution of the paper lies in the fact that, in contrast to most other work, it is quantitative and that it focuses on brokers identified in an actual network (based on both suppliers and users of the knowledge infrastructure). Studying the phenomenon of brokerage provides us with clear insights into the concept of brokerage regarding SME networks in different fields. In particular we highlight how third parties contribute to the transfer and development of knowledge. Empirical results show, among others that the most influential brokers are found in the nonprofit and science sector and have a long track record in their branch.
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De paper beschrijft de theoretische onderbouwing van het model Professionaliteit en Persoonlijk Leiderschap zoals dat door Fontys Hogeschool Marketing Management gebruikt wordt in haar onderwijs. De paper is uitgereikt tijdens een presentatie over dit onderwerp op het jaarcongres van de HBO-raad 2009.
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The Nutri-Score front-of-pack label, which classifies the nutritional quality of products in one of 5 classes (A to E), is one of the main candidates for standardized front-of-pack labeling in the EU. The algorithm underpinning the Nutri-Score label is derived from the Food Standard Agency (FSA) nutrient profile model, originally a binary model developed to regulate the marketing of foods to children in the UK. This review describes the development and validation process of the Nutri-Score algorithm. While the Nutri-Score label is one of the most studied front-of-pack labels in the EU, its validity and applicability in the European context is still undetermined. For several European countries, content validity (i.e., ability to rank foods according to healthfulness) has been evaluated. Studies showed Nutri-Score's ability to classify foods across the board of the total food supply, but did not show the actual healthfulness of products within different classes. Convergent validity (i.e., ability to categorize products in a similar way as other systems such as dietary guidelines) was assessed with the French dietary guidelines; further adaptations of the Nutri-Score algorithm seem needed to ensure alignment with food-based dietary guidelines across the EU. Predictive validity (i.e., ability to predict disease risk when applied to population dietary data) could be re-assessed after adaptations are made to the algorithm. Currently, seven countries have implemented or aim to implement Nutri-Score. These countries appointed an international scientific committee to evaluate Nutri-Score, its underlying algorithm and its applicability in a European context. With this review, we hope to contribute to the scientific and political discussions with respect to nutrition labeling in the EU.
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To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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