Funcionamiento familiar y estilos de vida saludables en los pobladores del Territorio Vecinal Municipal N° 5. Miramar, 2014.

El presente trabajo de investigación es un estudio descriptivo correlacional, realizado en el Territorio Vecinal Municipal N° 5, Consejo Menor de Miramar, distrito de Moche; el cual analiza la relación entre el funcionamiento familiar y los estilos de vida saludables de los pobladores, la población estuvo conformada por 380 familias, obteniéndose una muestra de 189 familias. Siguiendo el enfoque cuantitativo, para la recolección de datos se utilizó el APGAR familiar, para medir el funcionamiento familiar, elaborado por Smilkstein, y el Test Estilos de Vida Saludables, adaptado por las autoras, para medir los estilos de vida saludables en la familia, luego de procesada la información se aplicó la Prueba de Chi Cuadrado para medir la relación entre las variables de estudio. Los resultados más importantes revelan que: El 42.9% de familias presentó disfunción familiar leve; 41.8% normal funcionamiento familiar, el 10% disfunción familiar moderada; el 5.3% disfunción familiar severa; así mismo el 79.9% de familias tienen estilos de vida saludables altos y el 20.1% estilos de vida saludables bajos. Existe asociación entre funcionamiento familiar y estilos de vida (p= 0.002), en familias con buen funcionamiento familiar predominan los indicadores de afecto, crecimiento, participación; en el adecuado estilo de vida los indicadores más resaltantes son socialización y convivencia, higiene y responsabilidad en salud.The present research is a descriptive correlational study in the City Neighborhood Territory No. 5, Minor Council Miramar district of Moche; which analyzes the relationship between family functioning and healthy lifestyles of the inhabitants, the population consisted of 380 households, obtaining a sample of 189 households. Following the quantitative approach to data collection family APGAR was used to measure family functioning, prepared by Smilkstein, and Test Healthy Lifestyles, adapted by the authors to measure healthy lifestyles in the family, information processed after the Chi Square test was applied to measure the relationship between the study variables. The main results show that: 42.9 % of families had mild family dysfunction; 41.8 % normal family functioning, 10% moderate family dysfunction; 5.3% severe family dysfunction; 79.9 % of families have high healthy life styles and 20.1 % low healthy life styles, the association between family functioning and lifestyles ( p = 0.002 ) in families with good family functioning indicators affection, growth predominate, participation. At the right lifestyle the most striking indicators are socialization and coexistence, hygiene and health responsibility.Tesi

Privacy and Data Analytics in the growing digital sphere

The developments and convergence of technology have generated a complex digital eco-systems that create, share and process more than 2.5 quintillion bytes of data every day. Organizations and individuals are generating a vast digital footprint that is transforming not only the nature and role of information systems but also the fundamental way humans interact in modern society. Although some data is provided with explicit consent and users’ knowledge of how their data will be used, most data is tracked without users’ awareness and processed with a wide range of analytical tools in ways that are not transparent. Internet users have been empowered, they expect quality and convenience from each interaction they have with individuals and organizations through different systems and platforms. However, a critical aspect for users to trust these interactions is frequently neglected, fairness. Fairness could be analyzed at many different levels, we focus on organizations practices to manage individual’s data and its implications for the concept of privacy. While citizens expect all the benefits technology provide there is also an increasing concern on how data is used. Research indicates the critical dimensions of individuals’ concerns about their data are 1) security, 2) selling/sharing to third parties 3) collection of too much data 4) errors on individual’s data 5) lack of control over their own data. Furthermore, recent scandals on the topic, such as Cambridge Analytica and Facebook involvement, bring a new awareness of the many ways companies are monetizing personal data. What has been intriguing is that in most cases, actual behavior contradicts the high perceived levels of privacy concerns and users keep accepting terms and conditions without rationalizing the implications. A possible explanation is that for most users the benefits of sharing data outweigh the internet privacy concerns. Research has found that intrusiveness is a higher inhibitor for online engagement which has to be considered by organizations. Consumers of digital media experience intrusiveness when there is a disruption (temporal, visual, flow) of what users are doing. For example, a distraction when organizations’ marketing follows users across platforms, when users receive an interaction invite while busy in an important meeting, when consumers receive multiple e-mails that are entirely irrelevant or when users are forced to accept permission in order to use a website or an app, just to mention some. The imminent coming availability of 5G, will dramatically increase connectivity between individuals and things, posing significant challenges for the information systems field. Managing the growing eco-systems interaction and integration together with the ownership and management of data will become more complex. As a response to privacy concerns and intrusiveness, new data protection regulations are requiring more openness and transparency from organizations changing the way data is managed. The principles of the European General Data Protection Regulations (GDPR) that came to force in May 2018, provide an entire legal framework addressing the modern concept of privacy in a technologically powered world, aiming to give citizens more rights and control over personal data. However, the European approach to data protection varies to the practices in the USA and certainly are different in China, where the social credit score system represents an example of an entirely different paradigm to approach privacy. These contrasting views are also evident within the same country, by generations. People who lived before the information revolution, see privacy differently than those born after who tend to accept the tradeoffs more readily. This paper aims to present crucial arguments to redefine the concept of privacy, its implications for information systems, data governance and data analytics together with recommendations to organizations