The new automated daily mortality surveillance system

The experience reported in an earlier Eurosurveillance issue on a fast method to evaluate the impact of the 2003 heatwave on mortality in Portugal, generated a daily mortality surveillance system (VDM) that has been operating ever since jointly with the Portuguese Heat Health Watch Warning System. This work describes the VDM system and how it evolved to become an automated system operating year-round, and shows briefly its potential using mortality data from January 2006 to June 2009 collected by the system itself. The new system has important advantages such as: rapid information acquisition, completeness (the entire population is included), lightness (very little information is exchanged, date of death, age, sex, place of death registration). It allows rapid detection of impacts (within five days) and allows a quick preliminary quantification of impacts that usually took several years to be done. These characteristics make this system a powerful tool for public health action. The VDM system also represents an example of inter-institutional cooperation, bringing together organisations from two different ministries, Health and Justice, aiming at improving knowledge about the mortality in the population

The perils of automaticity

Classical theories of skill acquisition propose that automatization (i.e., performance requires progressively less attention as experience is acquired) is a defining characteristic of expertise in a variety of domains (e.g., Fitts & Posner, 1967). Automaticity is believed to enhance smooth and efficient skill execution by allowing performers to focus on strategic elements of performance rather than on the mechanical details that govern task implementation (Williams & Ford, 2008). By contrast, conscious processing (i.e., paying conscious attention to one’s action during motor execution) has been found to disrupt skilled movement and performance proficiency (e.g., Beilock & Carr, 2001). On the basis of this evidence, researchers have tended to extol the virtues of automaticity. However, few researchers have considered the wide range of empirical evidence which indicates that highly automated behaviors can, on occasion, lead to a series of errors that may prove deleterious to skilled performance. Therefore, the purpose of the current paper is to highlight the perils, rather than the virtues, of automaticity. We draw on Reason’s (1990) classification scheme of everyday errors to show how an overreliance on automated procedures may lead to 3 specific performance errors (i.e., mistakes, slips, and lapses) in a variety of skill domains (e.g., sport, dance, music). We conclude by arguing that skilled performance requires the dynamic interplay of automatic processing and conscious processing in order to avoid performance errors and to meet the contextually contingent demands that characterize competitive environments in a range of skill domains

Fostering reflection in the training of speech-receptive action

Dieser Aufsatz erörtert Möglichkeiten und Probleme der Förderung kommunikativer Fertigkeiten durch die Unterstützung der Reflexion eigenen sprachrezeptiven Handelns und des Einsatzes von computerunterstützten Lernumgebungen für dessen Förderung. Kommunikationstrainings widmen sich meistens der Förderung des beobachtbaren sprachproduktiven Handelns (Sprechen). Die individuellen kognitiven Prozesse, die dem sprachrezeptiven Handeln (Hören und Verstehen) zugrunde liegen, werden häufig vernachlässigt. Dies wird dadurch begründet, dass sprachrezeptives Handeln in einer kommunikativen Situation nur schwer zugänglich und die Förderung der individuellen Prozesse sprachrezeptiven Handelns sehr zeitaufwändig ist. Das zentrale Lernprinzip - die Reflexion des eigenen sprachlich-kommunikativen Handelns - wird aus verschiedenen Perspektiven diskutiert. Vor dem Hintergrund der Reflexionsmodelle wird die computerunterstützte Lernumgebung CaiMan© vorgestellt und beschrieben. Daran anschließend werden sieben Erfolgsfaktoren aus der empirischen Forschung zur Lernumgebung CaiMan© abgeleitet. Der Artikel endet mit der Vorstellung von zwei empirischen Studien, die Möglichkeiten der Reflexionsunterstützung untersucheThis article discusses the training of communicative skills by fostering the reflection of speech-receptive action and the opportunities for using software for this purpose. Most frameworks for the training of communicative behavior focus on fostering the observable speech-productive action (i.e. speaking); the individual cognitive processes underlying speech-receptive action (hearing and understanding utterances) are often neglected. Computer-supported learning environments employed as cognitive tools can help to foster speech-receptive action. Seven success factors for the integration of software into the training of soft skills have been derived from empirical research. The computer-supported learning environment CaiMan© based on these ideas is presented. One central learning principle in this learning environment reflection of one's own action will be discussed from different perspectives. The article concludes with two empirical studies examining opportunities to foster reflecti

ATM automation: guidance on human technology integration

© Civil Aviation Authority 2016Human interaction with technology and automation is a key area of interest to industry and safety regulators alike. In February 2014, a joint CAA/industry workshop considered perspectives on present and future implementation of advanced automated systems. The conclusion was that whilst no additional regulation was necessary, guidance material for industry and regulators was required. Development of this guidance document was completed in 2015 by a working group consisting of CAA, UK industry, academia and industry associations (see Appendix B). This enabled a collaborative approach to be taken, and for regulatory, industry, and workforce perspectives to be collectively considered and addressed. The processes used in developing this guidance included: review of the themes identified from the February 2014 CAA/industry workshop1; review of academic papers, textbooks on automation, incidents and accidents involving automation; identification of key safety issues associated with automated systems; analysis of current and emerging ATM regulatory requirements and guidance material; presentation of emerging findings for critical review at UK and European aviation safety conferences. In December 2015, a workshop of senior management from project partner organisations reviewed the findings and proposals. EASA were briefed on the project before its commencement, and Eurocontrol contributed through membership of the Working Group.Final Published versio

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation