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Reza Yousefi Nooraie, Maureen Dobbins

Reza Yousefi Nooraie, Maureen Dobbins

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Reza Yousefi Nooraie, Maureen Dobbins

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  1. A social network analysis to study the implementation of a knowledge translation interventionin a Canadian public health department Reza Yousefi Nooraie, Maureen Dobbins

  2. The complexity of evidence-informed decision-making in public health • the quality of evidence • community views • social and political pressures • organizational constraints

  3. The importance of social context • the effect of KT interventions could not be determined without considering the impact of context(LaRocca2012) • Social networks as opportunities and constraints • access to resources • collaboration and partnership • social influence • social norms

  4. Local opinion leaders (OLs) • someone who is able to informally affect others’ attitudes and behaviours in a desired way • is earned as a result of competence, accessibility, trustworthiness, and conformity to social norms. • A sociometric (network) approach to identifying opinion leaders

  5. Study context • An organization-wide KT intervention in threepublic health departments in Ontario • varied in size, complexity, and their commitment and types of policies for implementing EIDM.

  6. Tailored KT intervention • knowledge broker (KB) mentoring of small groups through the EIDM process • workshops • one-on-one consultation and support • 9-10 months

  7. Social network survey (baseline/follow-up) -Identify up to 5 staff in health department: • Information seeking: to whom you regularly turn to get help incorporating research evidence to inform practice • Expertise recognition: who are experienced and knowledgeable in finding research evidence and translating it into practice • Friendship: whom you consider as your personal friends

  8. highly engaged: Intensively involved in KT intervention • based on KB journal, attendance lists of workshops, and online survey • Opinion leaders (OLs): the highest quartile of centrality in information-seeking and expertise recognition networks • Highly engaged OLs

  9. Mixed-effects model • DV: Change in evidence-based practice implementation scores (MazurekMelnyk2008)over time • IV: • highly engaged in KT • seeking information from a highly engaged peer • seeking information from a highly engaged OL

  10. Change in EBP implementation behavior scores Not engaged in KT intervention, influenced by highly engaged OLs small cohesive group of highly engaged OLs

  11. Department A • EIDM a strategic priorityand committed resources • Managers and project specialists were requested to engage • 18Rapid Reviews • KB facilitated Critical Appraisal Clubs • Regular meetings

  12. Department A(n=268) • 47 highly engaged (18%) • 17 highly engaged OLs (36%) • managersand project specialists • also central in friendship network

  13. Improvement in EBP implementation scores

  14. Interpretation • The highly engaged opinion leaders improved the behavior of their peers • Opinion leaders who were highly engaged in KT, improved their behavior if they shaped a dense social network among themselves

  15. Recommendations • engagement of opinion leaders • role definition • Continuous nature of the intervention • formation of communities of practice