Findings provide insight into the mechanisms of tailoring with important implications for optimizing message design. (PsycINFO Database Record

While advances in highly targeted therapies and increased use of mammogram services have contributed to the overall decline of breast cancer deaths in the United States, these benefits have not been distributed equitably. Less educated, poor, rural, non-Hispanic African American women have poorer access to cancer services and are less likely to have had a mammogram than are urban women. Lack of physician recommendations and perceived barriers in accessing diagnostic services are major factors that hinder the uptake of regular mammograms in rural communities. This article reports results of formative research conducted as part of a larger study focused on the participatory development of an electronic reminder system for breast cancer screening. The article discusses insights gained from focus groups with rural patients and clinicians about their information needs, breast cancer screening behaviors, barriers to care, and mammography referral practices.

BackgroundThe World Wide Web has become a dissemination platform for scientific and non-scientific publications. However, most of the information remains locked up in discrete documents that are not always interconnected or machine-readable. The connectivity tissue provided by RDF technology has not yet been widely used to support the generation of self-describing, machine-readable documents.ResultsIn this paper, we present our approach to the generation of self-describing machine-readable scholarly documents. We understand the scientific document as an entry point and interface to the Web of Data. We have semantically processed the full-text, open-access subset of PubMed Central. Our RDF model and resulting dataset make extensive use of existing ontologies and semantic enrichment services. We expose our model, services, prototype, and datasets at http://biotea.idiginfo.org/ConclusionsThe semantic processing of biomedical literature presented in this paper embeds documents within the Web of Data and facilitates the execution of concept-based queries against the entire digital library. Our approach delivers a flexible and adaptable set of tools for metadata enrichment and semantic processing of biomedical documents. Our model delivers a semantically rich and highly interconnected dataset with self-describing content so that software can make effective use of it.

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A skin cancer screening programme involving 2150 employees based at the head office of a large UK retailer resulted in the detection of four melanomas at an early curable stage. In addition, three other malignant and three potentially malignant tumours were discovered, and individuals at greater risk of developing melanoma were identified and counselled accordingly.