The World Health Organization Multi-Professional Patient Safety Curriculum : implementation of key modules and its impact on patient safety knowledge, skills, and attitudes of medical students at the University of Algarve

ABSTRACT - Objectives: We attempted to show how the implementation of the key elements of the World Health Organization Patient Safety Curriculum Guide Multi-professional Edition in an undergraduate curriculum affected the knowledge, skills, and attitudes towards patient safety in a graduate entry Portuguese Medical School. Methods: After receiving formal recognition by the WHO as a Complementary Test Site and approval of the organizational ethics committee , the validated pre-course questionnaires measuring the knowledge, skills, and attitudes to patient safety were administered to the 2nd and3rd year students pursuing a four-year course (N = 46). The key modules of the curriculum were implemented over the academic year by employing a variety of learning strategies including expert lecturers, small group problem-based teaching sessions, and Simulation Laboratory sessions. The identical questionnaires were then administered and the impact was measured. The Curriculum Guide was evaluated as a health education tool in this context. Results: A significant number of the respondents, 47 % (n = 22), reported having received some form of prior patient safety training. The effect on Patient Safety Knowledge was assessed by using the percentage of correct pre- and post-course answers to construct 2 × 2 contingency tables and by applying Fishers’ test (two-tailed). No significant differences were detected (p < 0.05). To assess the effect of the intervention on Patient Safety skills and attitudes, the mean and standard deviation were calculated for the pre and post-course responses, and independent samples were subjected to Mann-Whitney’s test. The attitudinal survey indicated a very high baseline incidence of desirable attitudes and skills toward patient safety. Significant changes were detected (p < 0.05) regarding what should happen if an error is made (p = 0.016), the role of healthcare organizations in error reporting (p = 0.006), and the extent of medical error (p = 0.005). Conclusions: The implementation of selected modules of the WHO Patient Safety Curriculum was associated with a number of positive changes regarding patient safety skills and attitudes, with a baseline incidence of highly desirable patient safety attitudes, but no measureable change on the patient safety knowledge, at the University of Algarve Medical School. The significance of these results is discussed along with implications and suggestions for future research.

Adverse life events and coping skills in panic disorder

Research literature and clinical experience shows that panic patients are often able to identify stressors that preceded the onset of their first attacks. In this study we investigated the relation between life events, coping skills, and panic disorder. METHODS: Forty-tree panic patients were compared with 29 control subjects regarding the occurrence and the impact of stressful life events in a 1-year period preceding the onset of panic attacks using the Social Readjustment Rating Scale and London Life Event and Difficulty Schedule. Coping skills were measured using the Ways of Coping Questionnaire. RESULTS: No differences were observed between panic patients and controls regarding the number of reported stressful life events in the previous year. Panic patients compared to controls reported loss of social support as the most meaningful class of events significantly more often. In response to stressful situations, panic patients more often used coping skills judged as ineffective. CONCLUSIONS: The present study suggests that the type of life event and the coping skills used in response to them, more than the occurrence of stressful events itself, may be associated with the onset of panic disorder.

Graph usage in annual reports, evidence from Norwegian listed companies

As investors and other users of annual reports often focus their attention on graphs, it is important that they portray accurate and reliable information. However, previous studies show that graphs often distort information and mislead users. This study analyses graph usage in annual reports from the 52 most traded Norwegian companies. The findings suggest that Norwegian companies commonly use graphs, and that the graph distortions, presentational enhancement and measurement distortion, are present. No evidence of selectivity was found. This study recommends development of guidelines for graphical disclosure, and advises preparers and users of annual reports to be aware of misleading graphs.

A data-driven analytics approach in the study of pneumonia’s fatalities

As huge amounts of data become available in organizations and society, specific data analytics skills and techniques are needed to explore this data and extract from it useful patterns, tendencies, models or other useful knowledge, which could be used to support the decision-making process, to define new strategies or to understand what is happening in a specific field. Only with a deep understanding of a phenomenon it is possible to fight it. In this paper, a data-driven analytics approach is used for the analysis of the increasing incidence of fatalities by pneumonia in the Portuguese population, characterizing the disease and its incidence in terms of fatalities, knowledge that can be used to define appropriate strategies that can aim to reduce this phenomenon, which has increased more than 65% in a decade.

STAR: Speech Therapy with Augmented Reality for children with autism spectrum disorders

Graphics based systems of Augmented and Alternative Communication are widely used to promote communication in people with Autism Spectrum Disorders. This study discusses an integration of Augmented Reality in communication interventions, by relating elements of Augmented and Alternative Communication and Applied Behaviour Analysis strategies. An architecture for an Augmented Reality based interactive system to assist interventions is proposed. STAR provides an Augmented Reality tool to assist interventions performed by therapists and support for parents to join in and participate in the child’s intervention. Finally we report on the usage of the Augmented Reality tool in interventions with children with Autism Spectrum Disorders.

The impact of PBL on transferable skills development in management education

Attention to transferable skills is growing in higher education. Problem-based learning (PBL) is increasingly used in management education for its promising potential to, amongst others, promote transferable skills, including problem-solving, critical thinking and teamwork skills. However, this relationship is seldom assessed. In this study, I use structural equation modelling to examine the effectiveness of PBL, measured through students’ perceptions of satisfaction and skills development. Results show the development of transferable skills is explained by interaction with tutors and a host company, and defining teamwork rules. Satisfaction is explained by skills development, assessment issues, defining teamwork rules and understanding how organisations work. I draw conclusions and recommendations.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

Gamification e game-based learning: uma abordagem lúdica à aprendizagem

Relatório de estágio de mestrado em Ensino de Informática

Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"