The evolution of social networks through the implementation of evidence-informed decision-making interventions: a longitudinal analysis of three public health units in Canada

Implementation Science, Dec 2015

Background We studied the evolution of information-seeking networks over a 2-year period during which an organization-wide intervention was implemented to promote evidence-informed decision-making (EIDM) in three public health units in Ontario, Canada. We tested whether engagement of staff in the intervention and their EIDM behavior were associated with being chosen as information source and how the trend of inter-divisional communications and the dominance of experts evolved over time. Methods Local managers at each health unit selected a group of staff to get engage in Knowledge Broker-led workshops and development of evidence summaries to address local public health problems. The staff were invited to answer three online surveys (at baseline and two annual follow-ups) including name generator questions eliciting the list of the staff they would turn to for help integrating research evidence into practice. We used stochastic actor-oriented modeling to study the evolution of networks. We tested the effect of engagement in the intervention, EIDM behavior scores, organizational divisions, and structural dynamics of social networks on the tendency of staff to select information sources, and the change in its trend between year 1 and year 2 of follow-up. Results In all the three health units, and especially in the two units with higher levels of engagement in the intervention, the network evolved towards a more centralized structure, with an increasing significance of already central staff. The staff showed greater tendencies to seek information from peers with higher EIDM behavior scores. In the public health unit that had highest engagement and stronger leadership support, the engaged staff became more central. In all public health units, the engaged staff showed an increasing tendency towards forming clusters. The staff in the three public health units showed a tendency towards limiting their connections within their divisions. Conclusions The longitudinal analysis provided us with a means to study the microstructural changes in public health units, clues to the sustainability of the implementation. The hierarchical transformation of networks towards experts and formation of clusters among staff who were engaged in the intervention show how implementing organizational interventions to promote EIDM may affect the knowledge flow and distribution in health care communities, which may lead to unanticipated consequences.

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The evolution of social networks through the implementation of evidence-informed decision-making interventions: a longitudinal analysis of three public health units in Canada

Yousefi-Nooraie et al. Implementation Science The evolution of social networks through the implementation of evidence-informed decision-making interventions: a longitudinal analysis of three public health units in Canada Reza Yousefi-Nooraie 0 1 Maureen Dobbins 3 Alexandra Marin 2 Robert Hanneman 5 Lynne Lohfeld 4 0 Health Research Methodology Program, Faculty of Health Sciences, McMaster University , Hamilton , Canada 1 175 Longwood Road South , Suite 210a, Hamilton, ON L8P 0A1 , Canada 2 Department of Sociology, University of Toronto , Toronto , Canada 3 School of Nursing and Department of Clinical Epidemiology and Biostatistics, McMaster University , Hamilton , Canada 4 Department of Clinical Epidemiology and Biostatistics, McMaster University , Hamilton , Canada 5 Department of Sociology, College of Humanities, Arts, and Social Sciences, University of California , Riverside , USA Background: We studied the evolution of information-seeking networks over a 2-year period during which an organization-wide intervention was implemented to promote evidence-informed decision-making (EIDM) in three public health units in Ontario, Canada. We tested whether engagement of staff in the intervention and their EIDM behavior were associated with being chosen as information source and how the trend of inter-divisional communications and the dominance of experts evolved over time. Methods: Local managers at each health unit selected a group of staff to get engage in Knowledge Broker-led workshops and development of evidence summaries to address local public health problems. The staff were invited to answer three online surveys (at baseline and two annual follow-ups) including name generator questions eliciting the list of the staff they would turn to for help integrating research evidence into practice. We used stochastic actor-oriented modeling to study the evolution of networks. We tested the effect of engagement in the intervention, EIDM behavior scores, organizational divisions, and structural dynamics of social networks on the tendency of staff to select information sources, and the change in its trend between year 1 and year 2 of follow-up. Results: In all the three health units, and especially in the two units with higher levels of engagement in the intervention, the network evolved towards a more centralized structure, with an increasing significance of already central staff. The staff showed greater tendencies to seek information from peers with higher EIDM behavior scores. In the public health unit that had highest engagement and stronger leadership support, the engaged staff became more central. In all public health units, the engaged staff showed an increasing tendency towards forming clusters. The staff in the three public health units showed a tendency towards limiting their connections within their divisions. Conclusions: The longitudinal analysis provided us with a means to study the microstructural changes in public health units, clues to the sustainability of the implementation. The hierarchical transformation of networks towards experts and formation of clusters among staff who were engaged in the intervention show how implementing organizational interventions to promote EIDM may affect the knowledge flow and distribution in health care communities, which may lead to unanticipated consequences. Evidence-informed decision-making; Social network analysis; Stochastic actor-oriented modeling; Social selection; Longitudinal analysis Background Organizational innovations can affect social relations within networks. Implementation of innovations is a complex and dynamic process by which the people who are bound within relationships with each other in a social context make adjustments to achieve desired outcomes [ 1, 2 ]. Changes in personal and collective knowledge and attitudes over time may affect individuals’ choices for interaction, and subsequently affect network composition [3]. Interventions to promote evidence-informed decisionmaking (EIDM), like other organizational behavior change interventions, may have social consequences and affect how individuals interact with each other [ 3 ]. While implementation frameworks highlight the role of contextual and social factors [ 4 ], many identify them as barriers/facilitators of the process of EIDM and not the outcomes that are influenced by it [ 5–7 ]. We do not know much about how implementation of EIDM interventions affects the social structure of health care settings. In a recent 2-year study, we examined the informationseeking relationships of staff of three public health units in Ontario, Canada, before and after implementing a multi-faceted and site-tailored EIDM intervention. Our goal was to understand how information-seeking networks evolved over time and how engagement in the intervention and evidence-informed behavior of staff associated with their evolving network positions and relational tendencies. An organizational intervention to (...truncated)


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Reza Yousefi-Nooraie, Maureen Dobbins, Alexandra Marin, Robert Hanneman, Lynne Lohfeld. The evolution of social networks through the implementation of evidence-informed decision-making interventions: a longitudinal analysis of three public health units in Canada, Implementation Science, 2015, pp. 166, 10, DOI: 10.1186/s13012-015-0355-5