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      Scientists’ Prioritization of Communication Objectives for Public Engagement

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          Abstract

          Amid calls from scientific leaders for their colleagues to become more effective public communicators, this study examines the objectives that scientists’ report drive their public engagement behaviors. We explore how scientists evaluate five specific communication objectives, which include informing the public about science, exciting the public about science, strengthening the public’s trust in science, tailoring messages about science, and defending science from misinformation. We use insights from extant research, the theory of planned behavior, and procedural justice theory to identify likely predictors of scientists' views about these communication objectives. Results show that scientists most prioritize communication designed to defend science from misinformation and educate the public about science, and least prioritize communication that seeks to build trust and establish resonance with the public. Regression analyses reveal factors associated with scientists who prioritize each of the five specific communication objectives. Our findings highlight the need for communication trainers to help scientists select specific communication objectives for particular contexts and audiences.

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          Most cited references11

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          Cultural cognition of the risks and benefits of nanotechnology.

          How is public opinion towards nanotechnology likely to evolve? The 'familiarity hypothesis' holds that support for nanotechnology will likely grow as awareness of it expands. The basis of this conjecture is opinion polling, which finds that few members of the public claim to know much about nanotechnology, but that those who say they do are substantially more likely to believe its benefits outweigh its risks. Some researchers, however, have avoided endorsing the familiarity hypothesis, stressing that cognitive heuristics and biases could create anxiety as the public learns more about this novel science. We conducted an experimental study aimed at determining how members of the public would react to balanced information about nanotechnology risks and benefits. Finding no support for the familiarity hypothesis, the study instead yielded strong evidence that public attitudes are likely to be shaped by psychological dynamics associated with cultural cognition.
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            A randomised trial and economic evaluation of the effect of response mode on response rate, response bias, and item non-response in a survey of doctors

            Background Surveys of doctors are an important data collection method in health services research. Ways to improve response rates, minimise survey response bias and item non-response, within a given budget, have not previously been addressed in the same study. The aim of this paper is to compare the effects and costs of three different modes of survey administration in a national survey of doctors. Methods A stratified random sample of 4.9% (2,702/54,160) of doctors undertaking clinical practice was drawn from a national directory of all doctors in Australia. Stratification was by four doctor types: general practitioners, specialists, specialists-in-training, and hospital non-specialists, and by six rural/remote categories. A three-arm parallel trial design with equal randomisation across arms was used. Doctors were randomly allocated to: online questionnaire (902); simultaneous mixed mode (a paper questionnaire and login details sent together) (900); or, sequential mixed mode (online followed by a paper questionnaire with the reminder) (900). Analysis was by intention to treat, as within each primary mode, doctors could choose either paper or online. Primary outcome measures were response rate, survey response bias, item non-response, and cost. Results The online mode had a response rate 12.95%, followed by the simultaneous mixed mode with 19.7%, and the sequential mixed mode with 20.7%. After adjusting for observed differences between the groups, the online mode had a 7 percentage point lower response rate compared to the simultaneous mixed mode, and a 7.7 percentage point lower response rate compared to sequential mixed mode. The difference in response rate between the sequential and simultaneous modes was not statistically significant. Both mixed modes showed evidence of response bias, whilst the characteristics of online respondents were similar to the population. However, the online mode had a higher rate of item non-response compared to both mixed modes. The total cost of the online survey was 38% lower than simultaneous mixed mode and 22% lower than sequential mixed mode. The cost of the sequential mixed mode was 14% lower than simultaneous mixed mode. Compared to the online mode, the sequential mixed mode was the most cost-effective, although exhibiting some evidence of response bias. Conclusions Decisions on which survey mode to use depend on response rates, response bias, item non-response and costs. The sequential mixed mode appears to be the most cost-effective mode of survey administration for surveys of the population of doctors, if one is prepared to accept a degree of response bias. Online surveys are not yet suitable to be used exclusively for surveys of the doctor population.
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              Predicting scientists' participation in public life.

              This research provides secondary data analysis of two large-scale scientist surveys. These include a 2009 survey of American Association for the Advancement of Science (AAAS) members and a 2006 survey of university scientists by the United Kingdom's Royal Society. Multivariate models are applied to better understand the motivations, beliefs, and conditions that promote scientists' involvement in communication with the public and the news media. In terms of demographics, scientists who have reached mid-career status are more likely than their peers to engage in outreach, though even after controlling for career stage, chemists are less likely than other scientists to do so. In terms of perceptions and motivations, a deficit model view that a lack of public knowledge is harmful, a personal commitment to the public good, and feelings of personal efficacy and professional obligation are among the strongest predictors of seeing outreach as important and in participating in engagement activities.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                25 February 2016
                2016
                : 11
                : 2
                : e0148867
                Affiliations
                [1 ]Stan Richards School of Advertising & Public Relations, Moody College of Communication, The University of Texas at Austin, Austin, Texas, United States of America
                [2 ]Dept. of Advertising & Public Relations, Michigan State University, East Lansing, Michigan, United States of America
                Queen Mary University of London, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: AD JB. Performed the experiments: AD JB. Analyzed the data: AD JB. Contributed reagents/materials/analysis tools: AD JB. Wrote the paper: AD JB.

                Article
                PONE-D-15-43835
                10.1371/journal.pone.0148867
                4767388
                26913869
                434a7f97-2cef-4bac-baf2-fa85b5d26348
                © 2016 Dudo, Besley

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 October 2015
                : 24 January 2016
                Page count
                Figures: 1, Tables: 2, Pages: 18
                Funding
                This material is based in part upon work supported by the National Science Foundation under Grant Nos. AISL-1421723 and AISL-1421214. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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                Data are available in the paper and Supporting Information files. Data will also be available at the project website hosted by Michigan State University (still under development—the URL will be entered as a comment).

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