A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

军队医学科技人员身心健康与影响因素的研究

To study the relationship between mental-physical health and coping behavior,job stress and job motivation in medical laboratory scientists and technicians. A cross-section survey was conducted,using the anxiety rating scale(SAS), depression rating scale(SDS), coping behavior rating scale,self-rating subhealth scale,self-rating stress scale,and self-rating job motivation scale among 289 medical laboratory scientists and technicians with analysis of ANOVA and Regression. The study was carried out in three steps:step1 is to analyze the general situation of the mental-physical health.Step 2 is to analyze the features of the main influencial facotors.Step 3 is to study the influence of the relative factors on mental-physical health.The following are the results: 1.The anxiety and depression scores of the medical researchers and technicians were 30±6 and 37±8 respectively,both significantly lower than those of the national norm（34±6 and 42±11 respectively,both P<0.01）,which are of significant difference. The depression score of the associate chief technician was 42±7, significantly higher than those of the medical researchers and technicians with any other professional titles (all P<0.05）, and the anxiety score of the associate chief technicians was 32±7, significantly higher than that of the research fellows（28±4, P<0.05），with no significant difference to the score of the medical researchers and technicians with other professional titles. The depression score,anxiety score and the subhealth score of the age group of 36～49 were 39±9,31±7 and 32±9 respectively, both higher than that of the age group of 20～35 （36±8,29±6 and 29±7 respectively,both P<0.05）,while there is much diference among other age groups. And the subhealth status has nothing to do with marital status,education background and professional titles. 2.The coping behaviors,job stress and motivation of the military medical laboratory scientists and technicians. 1）Coping behaviors:The seeking help score of the associate chief technicians was 2.8±0.5,not significantly different from that of the associate research fellows（2.8±0.5）,but significantly lower than those of the medical researchers and technicians with other professional titles(all P<0.01),and whose self-blame score was higher than that of the research fellows,which is of significant difference.The self-blame score and the imagination score of the associate research fellows were 2.0±0.5 and 2.4±0.5 respectively, significantly higher than that of the research fellows(1.6±0.4, 2.1±0.4,both P<0.01). The seeking help score of the females was 3.1±0.5, significantly higher than that of the males(2.9±0.5, both P<0.01),and the solving problem score was 3.4±0.6,significantly lower than the male(3.6±0.7, both P<0.01).There is no any significant difference in coping behaviors among researchers and technicians of different marital status,education background and age groups. 2)Job stress: The score of job stress of the age group of 35~49 and 50~60 were 23±8 and 25±6 respectively,significantly higher than that of the 20~35(21±7,both P<0.05). There is no any significant difference in job stress among researchers and technicians of different marital status,gender,professional titles and education background. 3)The score of meeting personal desire for reputation and interests of the males was 19.6±5.4，significantly higher than that of the females(18.4±5.0, both P<0.05), while there is no significant difference in job motivation among researchers and technicians of different titles,education,age and marital status. 3.The relationship of the mental-physical health of the military medical laboratory scientists and technicians with their coping behaviors,job stress and motivations 1) Coping behaviors:Regression analysis showed that, the best predictors of subhealth were anxiety,depression and escaping (β=-0.40,β=0.23, β=0.14, both P<0.01).the best predictors of anxiety and depression were active coping behaviors and negative coping behaviors (β=-0.40,β=0.40, both P<0.01).The moderate(rational) coping behaviors is the best predictor only of anxiety and of significant difference. 2)job stress : Regression analysis demonstrated that anxiety,depression,job stress 2 and job stress 4 are the best predictor for subhealth;job stress 12 is the best retro-predictor for depression; job stress 3 and 8 are the best predictor for depression;and job stress 2 and 9 are the best predictor for anxiety ,which are of significant difference. 3)Motivation:Regression analysis demonstrated that depression is the best predictor for subhealth while motivation itself doesn't predict subhealth;with respect to the specific contents of motivation,"probing unknown rules and making contributions to the human society "is the best retro-predictor for depression and has nothing to do with anxiety prediction. 4) The combined influences of coping behaviors,job stress and motivations: Regression analysis showed that, the best predictors for subhealth were anxiety,depression and job stress(β=0.41,β=0.24, β=0.19, both P<0.01).the best predictors for anxiety and depression were active coping behaviors and negative coping behaviors (β=-0.40,β=0.40, both P<0.01).The moderate(rational) coping behaviors is the best predictor only for anxiety and of significant difference. CONCLUSIONS: Regression analysis showed that the best predictors for anxiety and depression were active coping behaviors and negative coping behaviors, the best predictors for subhealth were anxiety,depression and job stress.Coping behaviors, job stress and motivations have significant influences on the mental-physical health of the military medical researchers and and technicians. Among them,coping behavior is the most important factor while job stress and motivation follow. Seeking help more often,less self-blame ,imagination and job stress could help release the anxiety, depression and improve the subhealth of the medical researchers and technicians. The findings of this study indicate we should address the physical and mental health of the military laboratory researchers and technicians.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Design and implementation of robust systems for secure malware detection

Malicious software (malware) have significantly increased in terms of number and effectiveness during the past years. Until 2006, such software were mostly used to disrupt network infrastructures or to show coders’ skills. Nowadays, malware constitute a very important source of economical profit, and are very difficult to detect. Thousands of novel variants are released every day, and modern obfuscation techniques are used to ensure that signature-based anti-malware systems are not able to detect such threats. This tendency has also appeared on mobile devices, with Android being the most targeted platform. To counteract this phenomenon, a lot of approaches have been developed by the scientific community that attempt to increase the resilience of anti-malware systems. Most of these approaches rely on machine learning, and have become very popular also in commercial applications. However, attackers are now knowledgeable about these systems, and have started preparing their countermeasures. This has lead to an arms race between attackers and developers. Novel systems are progressively built to tackle the attacks that get more and more sophisticated. For this reason, a necessity grows for the developers to anticipate the attackers’ moves. This means that defense systems should be built proactively, i.e., by introducing some security design principles in their development. The main goal of this work is showing that such proactive approach can be employed on a number of case studies. To do so, I adopted a global methodology that can be divided in two steps. First, understanding what are the vulnerabilities of current state-of-the-art systems (this anticipates the attacker’s moves). Then, developing novel systems that are robust to these attacks, or suggesting research guidelines with which current systems can be improved. This work presents two main case studies, concerning the detection of PDF and Android malware. The idea is showing that a proactive approach can be applied both on the X86 and mobile world. The contributions provided on this two case studies are multifolded. With respect to PDF files, I first develop novel attacks that can empirically and optimally evade current state-of-the-art detectors. Then, I propose possible solutions with which it is possible to increase the robustness of such detectors against known and novel attacks. With respect to the Android case study, I first show how current signature-based tools and academically developed systems are weak against empirical obfuscation attacks, which can be easily employed without particular knowledge of the targeted systems. Then, I examine a possible strategy to build a machine learning detector that is robust against both empirical obfuscation and optimal attacks. Finally, I will show how proactive approaches can be also employed to develop systems that are not aimed at detecting malware, such as mobile fingerprinting systems. In particular, I propose a methodology to build a powerful mobile fingerprinting system, and examine possible attacks with which users might be able to evade it, thus preserving their privacy. To provide the aforementioned contributions, I co-developed (with the cooperation of the researchers at PRALab and Ruhr-Universität Bochum) various systems: a library to perform optimal attacks against machine learning systems (AdversariaLib), a framework for automatically obfuscating Android applications, a system to the robust detection of Javascript malware inside PDF files (LuxOR), a robust machine learning system to the detection of Android malware, and a system to fingerprint mobile devices. I also contributed to develop Android PRAGuard, a dataset containing a lot of empirical obfuscation attacks against the Android platform. Finally, I entirely developed Slayer NEO, an evolution of a previous system to the detection of PDF malware. The results attained by using the aforementioned tools show that it is possible to proactively build systems that predict possible evasion attacks. This suggests that a proactive approach is crucial to build systems that provide concrete security against general and evasion attacks.

Which of the three action-oriented corporate social responsibility (CSR) types is the most effective in improving the public attitude toward tobacco companies?

Dissertação apresentada à Universidade Fernando Pessoa como parte dos requisitos para a obtenção do grau de Mestre em Ciências da Comunicação, ramo de Marketing e Publicidade

Exploiting the bioactive potential of marine sponge microbiota

Endospore-forming bacteria are often isolated from different marine sponges, but their abundance varies, and they are frequently missed by culture-independent studies. Within endospore-formers, Bacillus are renowned for the production of antimicrobials and other compounds of medical and industrial importance. Although this group has been well studied in many different environments, very little is known about the actual diversity and properties of sporeformers associated with marine sponges. Identification of the endospore-forming bacteria associated with the marine sponges; Haliclona simulans, Amphilectus fucorum and Cliona celata, has uncovered an abundant and diverse microbial population composed of Bacillus, Paenibacillus, Solibacillus, Halobacillus and Viridibacillus species. This diversity appears to be overlooked by other non-targeted approaches where spore-formers are masked by more dominant species within the ecosystem. In addition to the identification of two antibiotic resistant plasmids, this bank of sporeformers produce a range of bioactive compounds. New antimicrobial compounds are urgently needed to combat the spread of multidrug resistant pathogens, as few new options are entering the drug discovery pipelines for clinical trials. Based on the results of this project, endospore-formers associated with marine sponges may hold the answer. The power of coupling functional based assays with genomic approaches has enabled us to identify a novel class 1 lantibiotic, subtilomycin, which is active against several clinically relevant pathogens. Subtilomycin is encoded in the genomes of all the marine sponge B. subtilis isolates analysed. They cluster together phylogenetically and form a distinct group from other sequenced B. subtilis strains. Regardless of its potential clinical relevance, subtilomycin may be providing these strains with a specific competitive advantage(s) within the stringent confines of the marine sponge environment. This work has outlined the industrial and biotechnological potential of marine sponge endospore-formers which appear to produce a cocktail of bioactive compounds. Genome sequencing of specific marine sponge isolates highlighted the importance of mining extreme environments and habitats for new lead compounds with potential therapeutic applications.

Training in and assessment of the procedural skills required to perform peripheral nerve blockade

The training and ongoing education of medical practitioners has undergone major changes in an incremental fashion over the past 15 years. These changes have been driven by patient safety, educational, economic and legislative/regulatory factors. In the near future, training in procedural skills will undergo a paradigm shift to proficiency based progression with associated requirements for competence-based programmes, valid, reliable assessment tools and simulation technology. Before training begins, the learning outcomes require clear definition; any form of assessment applied should include measurement of these outcomes. Currently training in a procedural skill often takes place on an ad hoc basis. The number of attempts necessary to attain a defined degree of proficiency varies from procedure to procedure. Convincing evidence exists that simulation training helps trainees to acquire skills more efficiently rather than relying on opportunities in their clinical practice. Simulation provides a safe, stress free environment for trainees for skill acquisition, generalization and transfer via deliberate practice. The work described in this thesis contributes to a greater understanding of how medical procedures can be performed more safely and effectively through education. The effect of feedback, provided to novices in a standardized setting on a bench model, based on knowledge of performance was associated with an increase in the speed of skill acquisition and a decrease in error rate during initial learning. The timing of feedback was also associated with effective learning of skill. A marked attrition of skills (independent of the type of feedback provided) was demonstrable 24 hrs after they have first been learned. Using the principles of feedback as described above, when studying the effect of an intense training program on novices of varied years of experience in anaesthesia (i.e. the present training programmes / courses of an intense training day for one or more procedures). There was a marked attrition of skill at 24 hours with a significant correlation with increasing years of experience; there also appeared to be an inverse relationship between years of experience in anaesthesia and performance. The greater the number of years of practice experience, the longer it required a learner to acquire a new skill. The findings of the studies described in this thesis may have important implications for the trainers, trainees and training bodies in the design and implementation of training courses and the formats of delivery of changing curricula. Both curricula and training modalities will need to take account of characteristics of individual learners and the dynamic nature of procedural healthcare.

Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.

As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.

A new instrument for measuring anticoagulation-related quality of life: development and preliminary validation.

BACKGROUND: Anticoagulation can reduce quality of life, and different models of anticoagulation management might have different impacts on satisfaction with this component of medical care. Yet, to our knowledge, there are no scales measuring quality of life and satisfaction with anticoagulation that can be generalized across different models of anticoagulation management. We describe the development and preliminary validation of such an instrument - the Duke Anticoagulation Satisfaction Scale (DASS). METHODS: The DASS is a 25-item scale addressing the (a) negative impacts of anticoagulation (limitations, hassles and burdens); and (b) positive impacts of anticoagulation (confidence, reassurance, satisfaction). Each item has 7 possible responses. The DASS was administered to 262 patients currently receiving oral anticoagulation. Scales measuring generic quality of life, satisfaction with medical care, and tendency to provide socially desirable responses were also administered. Statistical analysis included assessment of item variability, internal consistency (Cronbach's alpha), scale structure (factor analysis), and correlations between the DASS and demographic variables, clinical characteristics, and scores on the above scales. A follow-up study of 105 additional patients assessed test-retest reliability. RESULTS: 220 subjects answered all items. Ceiling and floor effects were modest, and 25 of the 27 proposed items grouped into 2 factors (positive impacts, negative impacts, this latter factor being potentially subdivided into limitations versus hassles and burdens). Each factor had a high degree of internal consistency (Cronbach's alpha 0.78-0.91). The limitations and hassles factors consistently correlated with the SF-36 scales measuring generic quality of life, while the positive psychological impact scale correlated with age and time on anticoagulation. The intra-class correlation coefficient for test-retest reliability was 0.80. CONCLUSIONS: The DASS has demonstrated reasonable psychometric properties to date. Further validation is ongoing. To the degree that dissatisfaction with anticoagulation leads to decreased adherence, poorer INR control, and poor clinical outcomes, the DASS has the potential to help identify reasons for dissatisfaction (and positive satisfaction), and thus help to develop interventions to break this cycle. As an instrument designed to be applicable across multiple models of anticoagulation management, the DASS could be crucial in the scientific comparison between those models of care.