CCTS

1. CCTS UIC Center For Clinical and Translational Science Using Survey Based Social Network Analysis to Establish an Evaluation Baseline and Detect Short-Term Outcomes of a Clinical and Translational Science Center Megan K. Haller, PhD Eric W. Welch, PhD November 4, 2011

2. CCTS UIC Center For Clinical and Translational Science

3. Name Generators During the past academic year (August 2009 – August 2010), who have been your closest collaborators ? • Faculty at U of I (5) • Faculty at other universities/colleges (5) • Post-docs (3) • PhD students (3) • Non-academic collaborators (5)

4. Name Interpreters For each individual you named… • Up to 21 names piped into 11 name interpreters covering 46 elements of relationship • Characteristics of relationship • Characteristics of alter • Type of collaboration(s) • Resources provided by alter to collaboration(s) • Impact of collaboration with this person

5. Non-network items Background/Demographics Research Activities - time/load - production - translational activities CCTS Activities Satisfaction with CCTS

6. Ego-centric network analysis • Response rate not as important as in whole network • Picture/network diagram can still be useful. • Dyadic (tie-level) analysis: • Cases are pairs of actors • Variables are attributes of the relationship between actors • Monadic (actor-level) analysis • Cases are actors • Does translational network size lead to CCTS use? • Network (group-level) analysis

7. Users Who Self-identify as Translational Researchers Legend: Ties = Named as “close collaborator” Black nodes = CCTS Users White nodes = Non-CCTS Users Larger nodes = Survey respondents Smaller nodes = Collaborators who are not also survey respondents Circle = UI Faculty Squares = Non UI Faculty Up-triangle = Post-doc Diamond = PhD Student Down-triangle = Non-academic

8. Translational Researchers who have NOT used CCTS Services Circle = UI Faculty Squares = Non UI Faculty Up-triangle = Post-doc Diamond = PhD Student Down-triangle = Non-academic Legend: Ties = Named as “close collaborator” Black nodes = CCTS Users White nodes = Non-CCTS Users Larger nodes = Survey respondents Smaller nodes = Collaborators who are not also survey respondents

9. CCTS Users Who DO NOT Self-identify as Translational Researchers Legend: Ties = Named as “close collaborator” Black nodes = CCTS Users White nodes = Non-CCTS Users Larger nodes = Survey respondents Smaller nodes = Collaborators who are not also survey respondents Circle = UI Faculty Squares = Non UI Faculty Up-triangle = Post-doc Diamond = PhD Student Down-triangle = Non-academic

10. A few pictures help to inform leaders that: • To the extent that they are interested in providing services to all translational researchers, they have only tapped into about half of the potential market. • Potential for “word of mouth” marketing or diffusion of awareness of CCTS services. • Providing services to more than just translational researchers.

12. Compared to the collaborators of non-Users, collaborators of CCTS Users are more likely to: • be in a different discipline than the user • do clinical research • provide clinical services • do translational research • work on clinical guidelines • And are less likely to: • work on working papers/presentations • work on journal articles

13. Compared to the collaborators of non-users, collaborators of CCTS users are more likely to provide… • clinical expertise • access to facilities • …and are less likely to: • provide data • help identify new research dissemination pathways • integrate concepts and ideas from across different perspectives • reviewed work prior to submission • invited the respondent to speak

14. Compared to the collaborations of non-Users, collaborations of CCTS users are more likely to have: • led to new clinical research activity • …and are less likely to have: • led to new types of interventions • changed the way research is communicated

15. Discussion • Users collaborate to gain access to clinical activities and expertise as opposed to more “traditional” forms of collaboration such as disciplinary knowledge and academic production. • Users collaborate more on upstream activity (e.g. clinical activity) than downstream (e.g. new interventions, dissemination).

16. Not Significantly Different Name Interpreters Composition: Named as Faculty Collaborator, Named as External Faculty Collaborator, Named as Post Doctoral Collaborator Named as PhD Student Collaborator Named as Non-academic collaborator Network Size Tie Strength: How many years have you known each other , How frequently were you in personal contact with each individual, The extent to which you understand each individuals area of expertise, Is a Close Friend Characteristics: Works in Community Settings, is Female Resource Provision: Provided Clinical Expertise, Provided access to equipment, Provided methodological or theoretical expertise, Interpreted research for broader audiences, Integrated diverse methods or approaches Helped you obtain support for clinical or translational research. Support: Introduces you to potential collaborators, Reviewed your work prior to submission, Helped you translate your research to a lay audience.

17. Not Significantly Different Name Interpreters Production: Research Grant Proposal, Research study, Product Development Patent Application Policy Report or other policy directed material, Materials for Public Media Education Activities Outputs/Outcomes: Led to new types of interventions, Changed the methods you use in your research, Changed the substantive focus of your research, Changed the way in which you communicate about your research

18. Next step • Build regression model with controls and assess whether being a CCTS user matters for outputs.