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Community problem-solving framed as a distributed information use environment: bridging research and practice

Keywords: This article results from a qualitative study of 1) information behavior in community problem-solving framed as a distributed information use environment and 2) approaches used by a best-practice library to anticipate information needs associated with community problem solving.

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Abstract:

Introduction. This article results from a qualitative study of 1) information behavior in community problem-solving framed as a distributed information use environment and 2) approaches used by a best-practice library to anticipate information needs associated with community problem solving. Method. Several approaches to data collection were used - focus groups, interviews, observation of community and library meetings, and analysis of supporting documents. We focused first on the information behaviour of community groups. Finding that the library supported these activities we sought to understand its approach. Analysis. Data were coded thematically for both information behaviour concepts and themes germane to problem-solving activity. A grounded theory approach was taken to capture aspects of the library staff's practice. Themes evolved from the data; supporting documentation - reports, articles and library communication - was also coded. Results. The study showed 1) how information use environment components (people, setting, problems, problem resolutions) combine in this distributed information use environment to determine specific information needs and uses; and 2) how the library contributed to the viability of this distributed information use environment. Conclusion. Community problem solving, here explicated as a distributed IUE, is likely to be seen in multiple communities. The library model presented demonstrates that by reshaping its information practice within the framework of an information use environment, a library can anticipate community information needs as they are generated and where they are most relevant.

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