In this paper we present an experiment which has been performed to validate a pragmatic-based, expert-based and basic-level ontology. These ontologies were created for use in an application which generates questions for ordinary people with the purpose to determine a crisis situation. All three ontologies have specific characteristics related to their method of creation. This experiment shows that using the basic-level ontology results in the fastest and least ambiguous determination of a crisis situation.
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Traditional information systems for crisis response and management are centralized systems with a rigid hierarchical structure. Here we propose a decentralized system, which allows citizens to play a significant role as information source and/or as helpers during the initial stages of a crisis. In our approach different roles are assigned to citizens. To be able to designate the different roles automatically our system needs to generate appropriate questions. On the basis of information theory and a restricted role ontology we formalized the process of question generation. Three consecutive experiments were conducted with human users to evaluate to what extent the questioning process resulted in appropriate role determination. The result showed that the mental model of human users does not always comply with the formal model underpinning the questions generation process.
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Objective: To gain insight into patient participation in general practice by examining if and how patients' question-asking behaviour has changed over the years (2007-2016). Methods: A random set of real-life video-recorded consultations collected in 2015-2016 (n = 437) was observed and compared with that of a former study in 2007-2008 (n = 533). Patients' question-asking behaviour was coded using an adapted RIAS protocol containing six categories: medical condition/therapeutic regimen; psychosocial; social context; lifestyle; ask for opinion doctor; practical. GPs and patients completed questionnaires about their background characteristics. Data were analysed using multi-level analysis. Results: Patients asked fewer questions in 2016 than in 2007. The type of question-asking behaviour changed significantly: in particular medical questions decreased while practical questions increased. Less educated patients asked significantly more practical questions than higher educated patients. Conclusion: Contrary to our expectations, patients' question-asking has decreased in 2016 compared to 2007, while the average consultation length has increased. The type of questions shifted from medical to practical, especially in less educated patients. It seems that GPs' professional role has expanded over time, since patients nowadays ask their GP more non-medical questions. Practice implications: GPs probably could continue facilitating patient involvement by more frequently using partnership-building and supportive communication.
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We investigate whether the automatic generation of questions from an ontology leads to a trustworthy determination of a situation. With our Situation Awareness Question Generator (SAQG) we automatically generate questions from an ontology. The experiment shows that people with no previous experience can characterize hectic situations rather fast and trustworthy. When humans are participating as a sensor to gather information it is important to use basic concepts of perception and thought.
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The propagandization of a Net Generation adds nothing to our understanding of the digital behaviour of young people. Indeed, it is becoming increasingly obvious that the whole concept of a Net Generation rests on incorrect assumptions. Hence, arguments based on a Net Generation are not only irrelevant and misleading but precarious as well. Precarious in the sense that they are mobilized as a decisive means of engineering change, not least in education policy. Only when we stop thinking in terms of the Net Generation can we form a more astute vision of when the deployment of digital learning aids will have a realistic chance of success.
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Background: Palliative needs in older patients are often not timely identified. The Surprise Question (SQ) ‘would I be surprised if this patient died in the next year?’ is a well-researched tool that could aid in this effort. Most studies thus far involved physicians or specialist nurses, however the predictive value of the SQ when used by general nurses caring for hospitalized older patients is unknown. Objectives: To assess the predictive value of the SQ when used by general nurses and student nurses, in determining one year mortality in acutely hospitalized older patients. Design: Observational cohort study with an one year follow-up. Setting: One academic and one regional hospital in the Netherlands. Participants: Patients ≥70 years acutely hospitalized for at least 48 hours. Methods: Registered nurses and student nurses answered the SQ with ‘No’ (a positive SQ), ‘Yes’ or ‘Don't know’. Data on student nurses was analysed separately. The sensitivity, specificity, negative- and positive predictive values were calculated. Furthermore, logistic regression was performed to determine the odds of death. Results: 66 registered nurses answered the SQ for 252 patients of whom 77 (30.6%) died in the year after inclusion. Respectively, 44%, 14% and 22% died within the ‘No’, ‘Yes’ and ‘Don't know’ group. 85% of patients who died during admission or in the first three months post-discharge were identified. The sensitivity and specificity were 76.7% and 56.6%. The positive and negative predictive values were 43.7% and 84.6 %. Compared to persons in whom the SQ was answered with yes, a no answer was associated with an 4.7 times increased odds of dying in the next 12 months (odds ratio 4.71, 95% CI 2.43-9.12, p<0.001). Additionally, 20 student nurses answered the SQ about 73 patients; sensitivity and specificity were 46.7% and 72.1%, with a positive and negative predictive value of 53.8% and 66.0% respectively. Conclusion: The usability of the Surprise Question in predicting 12-month mortality in older acutely admitted patients is limited, due to the high false positive rate. The SQ when used by non-specialized nurses identifies vulnerable patients with an increased mortality risk and can be used as a first step in assessing a patients’ palliative needs, but has limited use as a single criterion for referral to specialist palliative care.
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Using an ontology to automatically generate questions for ordinary people requires a structure and concepts com- pliant with human thought. Here we present methods to develop a pragmatic, expert-based and a basic-level ontology and a framework to evaluate these ontologies. Comparing these ontologies shows that expert-based ontologies are most easy to con- struct but lack required cognitive semantic characteristics. Basic-level ontologies have structure and concepts which are better in terms of cognitive semantics but are most expensive to construct.
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BACKGROUND: Today's nursing school applicants are considered “digital natives.” This study investigated students' views of new health care technologies. METHOD: In a cross-sectional survey among first-year nursing students, 23 common nursing activities and five telehealth nursing activities were presented along with three statements: “I consider this a core task of nursing,” “I look forward to becoming trained in this task,” and “I think I will do very well in performing this task.” RESULTS: Internet-generation nursing students (n = 1,113) reported a significantly (p ⩽ .001) less positive view of telehealth activities than of common nursing activities. Median differences were 0.7 (effect size [ES], −0.54), 0.4 (ES, −0.48), and 0.3 (ES, −0.39), measured on a 7-point scale. CONCLUSION: Internet-generation nursing students do not naturally have a positive view of technology-based health care provision. The results emphasize that adequate technology and telehealth education is still needed for nursing students. [J Nurs Educ. 2017;56(12):717–724.]
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Organizations feel an urgency to develop and implement applications based on foundation models: AI-models that have been trained on large-scale general data and can be finetuned to domain-specific tasks. In this process organizations face many questions, regarding model training and deployment, but also concerning added business value, implementation risks and governance. They express a need for guidance to answer these questions in a suitable and responsible way. We intend to offer such guidance by the question matrix presented in this paper. The question matrix is adjusted from the model card, to match well with development of AIapplications rather than AI-models. First pilots with the question matrix revealed that it elicited discussions among developers and helped developers explicate their choices and intentions during development.
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In this paper we present a system that generates questions from an ontology to determine a crisis situation by ordinary people using their mobile phone: the Situation Awareness Question Generator. To generate questions from an ontology we propose a formalization based on Situation Theory and several strategies to determine a situation as quickly as possible. A suitable ontology should comply with human categorization to enhance trustworthiness. We created three ontologies, i.e. a pragmatic-based ontology, an expert-based ontology and a basiclevel ontology. Several experiments, published elsewhere, showed that the basic-level ontology is most suitable.
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