Objective: This paper investigates the collective psychological empowerment of users of online health communities, which has been often overlooked in literature. Drawing on the theories of empowerment in the context of community psychology, it explores the factors, that are also an important characteristic of online health communities, that are associated with the collective psychological empowerment of online health community users. Methods: Four factors of collective empowerment were analysed and evaluated using multiple linear regression on the data collected at the end of 2010 through a web survey on a non-probability sample of active participants in the web forums on Med.over.net, the largest online health community in Slovenia. Among them 8.5% were male, 49.7% had some kind of university education and 41.5% were married respondents with a mean age of 35.1 years. Results: The study found that the theoretical model of factors adequately fits the data, explaining 23.4% of the variability of collective empowerment. Sense of community, organisational involvement in community activities, and perceived online health community participation in the wider environment contribute to the collective empowerment of users of the online health community Med.over.net. Conversely, the frequency of posting messages to online health forum discussions is not associated with collective empowerment. Conclusion: In order to improve the collective empowerment of users of online health communities, it is necessary first of all to build on the quality of relationships between its members, involve them in strategic decisions of the community and foster online health community involvement in the wider social environment, since the participation of users in online communities itself does not lead to a higher level of their collective empowerment.
COBISS.SI-ID: 32384605
One of the central considerations in the theory of Markov chains is their convergence to an equilibrium. Coefficients of ergodicity provide an efficient method for such an analysis. Besides giving sufficient and sometimes necessary conditions for convergence, they additionally measure its rate. In this paper we explore coefficients of ergodicity for the case of imprecise Markov chains. The latter provide a convenient way of modelling dynamical systems where parameters are not determined precisely. In such cases a tool for measuring the rate of convergence is even more important than in the case of precisely determined Markov chains, since most of the existing methods of estimating the limit distributions are iterative. We define a new coefficient of ergodicity that provides necessary and sufficient conditions for convergence of the most commonly used class of imprecise Markov chains.
COBISS.SI-ID: 31933021