Suchergebnisse

In recent years a number of important science policy issues have rentered on questions about the social utility of science. The field of knowledge systems accounting has evolved as a special form of social impact assessment to observe and measure the impact of science on society. A system of social impacts of science (SIS) indicators has been developed as an attempt to represent these complex patterns and relationships. In the final analysis, the causal relevance of science to social performance depends on our capacity to link the complex knowledge system of modern science to the achievement of social goals.

The task of designing social impact of science (SIS) indicators is an ill-structured or systemic problem involving competing design goals, indeterminate design states, unspecified design rules, and an unbounded design space. These features of the problem are not a result of imperfections of measurement alone; they are due primarily to properties of the knowledge system that make it resemble a tangled river delta (anastomotic reticulum) in which different functional patterns (serial, parallel, assembly, arborescent, segmented, cyclic) coexist. The stunning complexity of knowledge systems makes it difficult but nevertheless possible to develop SIS indicators that are policy relevant by virtue of their being at once relational, causal, and normative (see also Peters, this volume). Any attempt to improve the policy relevance of impact indicators will recognize that systemic problems require nonconventional solutions based on principles of externalization, formalization, and simplification. An initial attempt to externalize the design process yields typologies of science output indicators and social impact indicators that may be conjoined to form social impact of science (SIS) indicators. By formalizing rules for making and challenging causal inferences, we can formulate rival hypotheses about the role of knowledge functions and structures in mediating the impacts of science on the achievement of social goals. By simplifying the design process we can maximize the likelihood that SIS indicators and the basis for their construction are widely comprehended by groups that have a stake in the social performance of science.

Clinicians' beliefs and awareness of treatment techniques may influence what innovations are perceived as needed and adoption of them. Clinicians at research-affiliated programs, however, may differ from clinicians at programs without research affiliation. We surveyed 162 clinicians at 15 substance abuse treatment programs (five research-affiliated programs and 10 matched nonresearch-affiliated programs) on addiction treatment belief items and awareness of Clinical Trials Network treatment innovations currently being tested. The research-affiliated clinicians had a higher percentage of clinicians with advanced degrees. In bivariate analyses, three differences in beliefs and four differences in awareness by research affiliation were found. Most of these differences disappeared during multivariate analyses. The results suggest that beliefs and awareness toward addiction treatment of research-affiliated clinicians, when controlling for demographic and professional characteristics, may be similar to those of other clinicians. This similarity should help in wider dissemination for those innovations found acceptable to clinicians at research-affiliated programs.