Social networks’ influence on KT in long term care: KT Canada Seminar Series

1. Social networks’ influence on KT in long term care: KT Canada Seminar Series Anne Sales, University of Alberta

2. Objectives • Brief overview of social network theory • Current study and social network sub-study • Current status • Response rates • Lessons learned to date • Next steps • Application to KT theory and practice

3. Long history of social science theory related to social networks • Both sociology and anthropology have histories– somewhat different • Date back to early part of 20th century • Hawthorne wiring room studies in the Western Electric Company in Chicago– 1920s • Very difficult to do anything other than quite small scale social network studies until computing power became widely available

4. Application to knowledge utilization/innovation in health care • History here also • Coleman and Katz (Chicago)– study of diffusion of medical innovation from the early 1950s • Explored the networks of physicians using a new antibiotic • Early, mid- and late adopters had different positions in social networks • Data have been reanalyzed and reinterpreted (more than once) Coleman J, Katz E, Menzel H. The Diffusion of an Innovation among Physicians. Sociometry 1957;20(4):253-70.

5. How this may apply in health care: view from an organizational perspective

6. Characteristics of networks • Issues of measurement • Fundamentally different level from the individual or organization • Unit of measurement is the relationship, not the individual • Relationships require a minimum of two people • Issues of analysis

7. Characteristics of relationships • Relationships have qualities • Existence • Strength • Direction • Symmetry/reciprocity • Directness • Hierarchy • Embeddedness • Structural properties • Holes • Density

8. There are different kinds of networks • Friendship • Volitional • Mutual but may not be reciprocal beyond a dyad • Varying content • Professional • May or may not be fully volitional • May or may not be mutual • Professional content • Advice-seeking • Often not wholly volitional • Usually not reciprocal • Work or professional related content • Mutual aid • Similar to advice seeking but may be more mutual and reciprocal

9. Brief notes on measuring social networks • Establishing boundaries is important • Work units have appeal • Typically finite, have clear demarcation • Can use lists of names from work unit • May or may not be simple depending on the organization • Different approaches to obtaining network data • Give people enumerated lists of names with some space to add • Ask people to enumerate/name based on specific prompts • Ask for limited number (usually 3 or 4) nominations (typically used in opinion leader surveys)

10. The DICE project • Data for Improvement and Clinical Excellence • Funded by CHSRF and Alberta Heritage Foundation for Medical Research • Three phase study • Phase One: Audit with feedback intervention in long term care organizations using RAI 2.0 data • Annual and quarterly assessment of all long term care residents– extensive assessment covering multiple domains • Feedback focuses on pain management, depression screening, and falls risk and prevalence • Phase Two: Similar process in home care • Phase Three: Spread across province depending on outcomes of first two phases

11. Current status of DICE • Have almost completed first phase (long term care) • Currently distributing 11th monthly feedback report to all staff in four Edmonton-area facilities • Have conducted post-feedback survey after most monthly feedback report distributions • Of note, post-feedback survey includes section to assess TPB domains and intent to change behavior • Surveys have been anonymous until social networks • Research staff have been engaged in these four facilities for about a year • Well known to staff, recognized

12. Social network sub-study • Funded by CIHR • Co-applicant: Carole Estabrooks; collaborator: Tom Valente (University of Southern California) • Include two of the four facilities in the DICE study • Rationale was that these two facilities have had longest contact with our research team; more familiar with research than other two facilities • One year tools and methods funding • Goals include • Develop social network questionnaire • Assess response • Assess social networks • Focus on impact on uptake of feedback reports • Current status • Have completed first round of surveys in two facilities • Very preliminary report today– focus on response and demographics

13. The survey • Five questions + demographics • Specific network types • Who you work with • Who you talk to at least daily • Who you discuss resident care with • Who you go to for advice about work • Who you discussed feedback report with • Did discussion make you feel positive, negative, or neutral about feedback report? • Coupled with post-feedback survey including TPB section

14. Survey format includes names of all staff on unit

15. Question about feedback report discussion

16. Census approach to social network measurement • Need clear boundaries to know which names to include • Nursing units may be ideal for this purpose • Requires putting names on questionnaires • Sensitivity– not anonymous • Requires storage for required time period • May decrease response rate • Increases length of questionnaire • Permits relatively accurate assessment of response rate

17. Alternatives • Recall only • “Please list all the people you worked with in the last two weeks…” • Very unlikely to be able to recall • Can be supplemented by staff list which isn’t integrated into the questionnaire • Has the advantage of being more confidential • Probably would take longer to complete • Requires writing in staff name or code

18. Response to questionnaire • Good response in one facility • About 65% of those who were working during shifts when we administered surveys • Varied by unit • Sub-optimal response in the other facility • Somewhere between 40 and 25% • Also varied widely by unit • This facility had just had audit for three days • We did the main survey on the Friday before Halloween– resident party competing priority • Have not yet left surveys to be returned by mail or drop box • Our experience suggests this would boost response by 3-5%

19. Response by provider type • Reasonable response by RNs, LPNs and health care aides (40-60% of possible responses) • Slightly lower response by allied health care professionals • This is important because of prior social network studies in LTC that indicate that there is marked segregation by provider type • Cott C. Structure and meaning in multidisciplinary teamwork. Sociol Health Illn 1998;20(6):848-73. • Cott C. "We decide, you carry it out": a social network analysis of multidisciplinary long-term care teams. Soc Sci Med 1997;45(9):1411-21.

20. Lessons to date • Social network data collection is feasible • This was not entirely clear • There have been some very poor response rates in previous attempts to do social network studies in KT • Embedding in an existing study with existing relationships is probably essential • Some risk– not all people view researchers in positive light; but most staff are interested and engaged in the work we’re doing • However, achieving really good response rates is hard and may or may not be worth effort

21. Some issues about response and social networks • Response rates matter differentially in assessing different types of relationships • “Who I worked with” is symmetrical • Reasonable to assume that if I say I worked with X, X would also say s/he worked with me • But “Who I go to for advice” is probably not symmetrical • Advice-seeking is usually not a mutual, symmetrical activity • Higher response rates are critical to assessing this network structure

22. Next steps in this project • More data collection in the low response site • Will try to do this soon • With flu season, we will be lucky to get this assessment completed before the end of the year • Analysis • Specific software to graph networks • Measures of density, centrality and other factors related to network structure can be done using Stata • More to come…

23. Some issues in analyzing social network data • SNA does not use traditional individual attribute analysis techniques • Fundamentally different kind of data– measures relationships, not aspects or attributes of individuals • Learning curve for software and analytic techniques • Different level as well as type of analysis • Not entirely clear how best to move between these levels • Many social network analysts believe that analyzing at an individual level is inappropriate • Multi-level techniques may be useful although it’s not entirely clear how to assign the level of the network • Different networks may be different levels

24. Opportunities for implementation research • New information that may be quite important in understanding uptake of interventions • May offer opportunities to adapt interventions based on social network findings

25. Opportunities for managing health care groups and organizations • Much social network analysis and research has come from organizational consulting • Relatively long history of using SNA to respond to organizational problems and issues • Varying effectiveness depending on what is done with network information • Independent of other interventions, networks themselves can be manipulated in an organizational context • This is not new • Measuring, analyzing, and understanding is relatively new

26. Network characteristics that may lend themselves to intervention • Structural holes • Places where there should be connections but there aren’t • Weak lattices • Connections exist but are weaker or more negative than they could/should be • Hierarchies • Specific design that may or may not be intentional • Health care is highly hierarchical • Cliques • Groups that have patterns of not communicating • Withholding information for purposes of power • Also probably very common in health care West E, Barron DN, Dowsett J, Newton JN. Hierarchies and cliques in the social networks of health care professionals: Implications for the design of dissemination strategies. Soc Sci Med 1999;48(5):633-46.

27. Teams and networks • Teamwork is frequently discussed in health care settings • Poorly understood • Evidence for high functioning teams in health care and how to foster them is sparse • Interprofessional/interdisciplinary teams are particularly of interest and highly problematic • Lots of rhetoric, relatively little evidence • Networks within teams are an important area of future focus Zwarenstein M, Reeves S. Working together but apart: barriers and routes to nurse--physician collaboration. Jt Comm J Qual Improv 2002;28(5):242,7, 209.

28. Cautions in using networks • Be mindful of unintended consequences • The way you ask questions conditions the responses you get • There are different kinds of networks • Some may not be relevant to what you want/need to achieve • The group of people I have coffee with may not be highly related to the group of people I would go to for help with a work related problem • Negative opinion leadership/negative effect networks are very common • There is a very large literature on networks • http://www.insna.org/

29. Already in partial use in implementation research • Opinion leader interventions • Typically identify opinion leader through surveys of participants in a given group • Surveys (usually) ask questions based on the Hiss instrument, derived from the Coleman and Katz study • Please name up to three individuals whom you would go to for information about…/whose opinion you would value about…/whom you would regard as an expert in… • Boundaries of the group are rarely clearly defined • Once identified, the interventions vary • Most often education as the primary vehicle • Outcomes have been mixed Valente TW. Social network thresholds in the diffusion of innovations. Soc Netw 1996;18(1):69-89. Doumit G, Gattellari M, Grimshaw J, O'Brien MA. Local opinion leaders: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2007;(1)

30. Issues raised by opinion leader interventions • Grimshaw et al. attempted to identify common opinion leaders across health /disease conditions and across professional groups • Unable to do so • Effectiveness of opinion leader approaches depends on accurate identification of opinion leaders– or does it? • May depend on other factors such as what type of group and their reliance on expertise • May depend on factors related to disease or health problem and the evidence • Vast majority of opinion leader studies have been among physicians who may be quite different from other health care providers Grimshaw JM, Eccles MP, Greener J, Maclennan G, Ibbotson T, Kahan JP et al. Is the involvement of opinion leaders in the implementation of research findings a feasible strategy? Implement Sci 2006;1:3.

31. How might networks interact with theories of behavior change? • Theory of planned behavior • Individual level theory designed to explain and/or predict individual behavior change (or lack of change) • Largely mediated through intention to change • Important set of variables in TPB relates to social and/or professional norms (Godin et al. Implementation Science 2008) • TPB and other individual level theories are silent on where and how norms are formed or how they c an be changed • There is also an issue of “perceived behavioral control” • Probably mostly an individual attribute, but may also be influenced by social network • Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991;50:179-211. • Godin G, Kok G. The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion 1996;11(2):87-98.

32. Intervention Professional norms Perception of intervention Attitudes towards behavior Intention to change behavior Social networks Behavior Subjective or social norms Perceived behavioral control

33. Opportunities to explore relationships between theoretical perspectives • We will be able to merge TPB data with SNA data and assess relationships among constructs and relationships • Opportunity to assess networks and uptake of feedback reports • Ultimately can look at association with resident outcomes but this is quite distal

34. Stay tuned… • We should have more findings in the next 8-12 months • Contact information: • anne.sales@ualberta.ca