Keeping citizens, companies, and organizations informed by Web KnowARR

For the main part, electronic government (or e-government for short) aims to put digital public services at disposal for citizens, companies, and organizations. To that end, in particular, e-government comprises the application of Information and Communications Technology (ICT) to support government operations and provide better governmental services (Fraga, 2002) as possible with traditional means. Accordingly, e-government services go further as traditional governmental services and aim to fundamentally alter the processes in which public services are generated and delivered, after this manner transforming the entire spectrum of relationships of public bodies with its citizens, businesses and other government agencies (Leitner, 2003). To implement this transformation, one of the most important points is to inform the citizen, business, and/or other government agencies faithfully and in an accessible way. This allows all the partaking participants of governmental affairs for a transition from passive information access to active participation (Palvia and Sharma, 2007). In addition, by a corresponding handling of the participants' data, a personalization towards these participants may even be accomplished. For instance, by creating significant user profiles as a kind of participants' tailored knowledge structures, a better-quality governmental service may be provided (i.e., expressed by individualized governmental services). To create such knowledge structures, thus known information (e.g., a social security number) can be enriched by vague information that may be accurate to a certain degree only. Hence, fuzzy knowledge structures can be generated, which help improve governmental-participants relationship. The Web KnowARR framework (Portmann and Thiessen, 2013; Portmann and Pedrycz, 2014; Portmann and Kaltenrieder, 2014), which I introduce in my presentation, allows just all these participants to be automatically informed about changes of Web content regarding a- respective governmental action. The name Web KnowARR thereby stands for a self-acting entity (i.e. instantiated form the conceptual framework) that knows or apprehends the Web. In this talk, the frameworks respective three main components from artificial intelligence research (i.e. knowledge aggregation, representation, and reasoning), as well as its specific use in electronic government will be briefly introduced and discussed.

Use of modern and traditional products to self-treat symptoms of sexually transmitted infections in South African women

The objective of the study is to investigate products used by women self-treating symptoms of reproductive tract infections (RTIs), including sexually transmitted infections (STIs), and their methods of administration. A household survey using a multi-stage cluster sample design was undertaken in KwaZulu-Natal, South Africa. Women aged 18-60 years were interviewed (n = 867) and information was collected on demographics, reproductive health and sexual behaviours. A fifth of women reported having RTI/STI symptoms (20.5%), of whom 41.9% were treating these symptoms (mostly discharge [79.1%], ulcers [6.8%] and itching [7.7%]). Only three women were using medication prescribed by a health provider, while the remainder were self-treating using traditional medicines and modern products, including antiseptics, soaps, petroleum jelly, menthol creams and alum. Products were administered in various ways. Although RTI/STI treatment is widely available and free in public health facilities, many women are still self-treating. Potential harm of products for self-treatment requires further investigation and efforts should be made to improve STI service uptake.

Assessment of smoking behaviour in the dental setting. A study comparing self-reported questionnaire data and exhaled carbon monoxide levels

The present study validated the accuracy of data from a self-reported questionnaire on smoking behaviour with the use of exhaled carbon monoxide (CO) level measurements in two groups of patients. Group 1 included patients referred to an oral medicine unit, whereas group 2 was recruited from the daily outpatient service. All patients filled in a standardized questionnaire regarding their current and former smoking habits. Additionally, exhaled CO levels were measured using a monitor. A total of 121 patients were included in group 1, and 116 patients were included in group 2. The mean value of exhaled CO was 7.6 ppm in the first group and 9.2 ppm in the second group. The mean CO values did not statistically significantly differ between the two groups. The two exhaled CO level measurements taken for each patient exhibited very good correlation (Spearman's coefficient of 0.9857). Smokers had a mean difference of exhaled CO values of 13.95 ppm (p < 0.001) compared to non-smokers adjusted for the first or second group. The consumption of one additional pack year resulted in an increase in CO values of 0.16 ppm (p = 0.003). The consumption of one additional cigarette per day elevated the CO measurements by 0.88 ppm (p < 0.001). Based on these results, the correlations between the self-reported smoking habits and exhaled CO values are robust and highly reproducible. CO monitors may offer a non-invasive method to objectively assess current smoking behaviour and to monitor tobacco use cessation attempts in the dental setting.

Predicting the risk of hospital admission in older persons--validation of a brief self-administered questionnaire in three European countries

OBJECTIVES: To validate the Probability of Repeated Admission (Pra) questionnaire, a widely used self-administered tool for predicting future healthcare use in older persons, in three European healthcare systems. DESIGN: Prospective study with 1-year follow-up. SETTING: Hamburg, Germany; London, United Kingdom; Canton of Solothurn, Switzerland. PARTICIPANTS: Nine thousand seven hundred thirteen independently living community-dwelling people aged 65 and older. MEASUREMENTS: Self-administered eight-item Pra questionnaire at baseline. Self-reported number of hospital admissions and physician visits during 1 year of follow-up. RESULTS: In the combined sample, areas under the receiver operating characteristic curves (AUCs) were 0.64 (95% confidence interval (CI)=0.62-0.66) for the prediction of one or more hospital admissions and 0.68 (95% CI=0.66-0.69) for the prediction of more than six physician visits during the following year. AUCs were similar between sites. In comparison, prediction models based on a person's age and sex alone exhibited poor predictive validity (AUC or= 0.5) were 2.3 times as likely (95% CI=2.1-2.6) as low-risk individuals to have a hospital admission, and 2.1 times as likely (95% CI=2.0-2.2) to have more than six physician visits. CONCLUSION: The Pra instrument exhibits good validity for predicting future health service use on a population level in different healthcare settings. Administrative data have shown similar predictive validity, but in practice, such data are often not available. The Pra is likely of high interest to governments and health insurance companies worldwide as a basis for programs aimed at health risk management in older persons.