Influence network linkages across implementation strategy conditions in a randomized controlled trial of two strategies for scaling up evidence-based practices in public youth-serving systems

Implementation Science

Patricia Chamberlain²  Thomas W Valente⁴  C Hendricks Brown⁵  Eric Rice³  Ian W Holloway¹  Lawrence A Palinkas³ 

[1] Department of Social Welfare, Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, USA;Oregon Social Learning Center, Eugene, OR, USA;School of Social Work, University of Southern California, Los Angeles, CA, USA;Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA;Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

关键词: Evidence-based practice;    Social networks;    Design;    Randomized controlled trial;    Implementation;   

Others  :  1211875
DOI  :  10.1186/1748-5908-8-133

 received in 2013-04-01, accepted in 2013-10-16,  发布年份 2013

【 摘 要 】

Background

Given the importance of influence networks in the implementation of evidence-based practices and interventions, it is unclear whether such networks continue to operate as sources of information and advice when they are segmented and disrupted by randomization to different implementation strategy conditions. The present study examines the linkages across implementation strategy conditions of social influence networks of leaders of youth-serving systems in 12 California counties participating in a randomized controlled trial of community development teams (CDTs) to scale up use of an evidence-based practice.

Methods

Semi-structured interviews were conducted with 38 directors, assistant directors, and program managers of county probation, mental health, and child welfare departments. A web-based survey collected additional quantitative data on information and advice networks of study participants. A mixed-methods approach to data analysis was used to create a sociometric data set (n = 176) to examine linkages between treatment and standard conditions.

Results

Of those network members who were affiliated with a county (n = 137), only 6 (4.4%) were directly connected to a member of the opposite implementation strategy condition; 19 (13.9%) were connected by two steps or fewer to a member of the opposite implementation strategy condition; 64 (46.7%) were connected by three or fewer steps to a member of the opposite implementation strategy condition. Most of the indirect steps between individuals who were in different implementation strategy conditions were connections involving a third non-county organizational entity that had an important role in the trial in keeping the implementation strategy conditions separate. When these entities were excluded, the CDT network exhibited fewer components and significantly higher betweenness centralization than did the standard condition network.

Conclusion

Although the integrity of the RCT in this instance was not compromised by study participant influence networks, RCT designs should consider how influence networks may extend beyond boundaries established by the randomization process in implementation studies.

Trial registration

NCT00880126

【 授权许可】

   
2013 Palinkas et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Berwick D: The science of improvement. JAMA 2008, 299(10):1182.
• [2]Glasgow RE, Magid D, Beck A, Ritzwoller D, Estabrooks P: Practical clinical trials for translating research to practice: Design and measurement recommendations. Med Care 2005, 43(6):551-557.
• [3]Proctor E, Landsverk J, Aarons G, Chambers D, Glisson C, Mittman B: Implementation research in mental health services: An emerging science with conceptual, methodological, and training challenges. Adm Pol Ment Hlth 2009, 36:24-34.
• [4]Landsverk J, Brown CH, Chamberlain P, Palinkas LA, Horwitz SM: Design and analysis in dissemination and implementation research. In Dissemination and implementation research in health: Translating science to practice. Edited by Brownson RC, Colditz GA, Proctor EK. New York: Oxford University Press; 2012:225-260.
• [5]Landsverk J, Brown C, Rolls Reutz J, Palinkas L, Horwitz S: Design elements in implementation research: A structured review of child welfare and child mental health studies. Adm Pol Ment Hlth 2011, 38:54-63.
• [6]Chamberlain P, Saldana L, Brown CH, Leve LD: Implementation of MTFC in California: A randomized trial of an evidence-based practice. In Empirically Supported Interventions for Community and Organizational Change. Edited by Roberts-DeGennaro M, Fogel SJ. Inc: Lyceum Books; 2010.
• [7]Chamberlain P, Brown CH, Saldana L, Reid J, Wang W, Marsenich L, Sosna T, Padgett C, Bouwman G: Engaging and recruiting counties in an experiment on implementing evidence-based practice in California. Adm Pol Ment Hlth 2008, 35:250-260.
• [8]Palinkas LA, Holloway IW, Rice E, Fuentes D, Wu Q, Chamberlain P: Social networks and implementation of evidence-based practices in public youth-serving systems: a mixed methods study. Implement Sci 2011, 6:113.
• [9]Wang W, Saldana L, Brown CH, Chamberlain P: Factors that influenced county system leaders to implement an evidence-based program: A baseline survey within a randomized controlled trial. Implement Sci 2010, 5:72.
• [10]Chamberlain P, Leve LD, Degarmo DS: Multidimensional treatment foster care for girls in the juvenile justice system: 2-year follow-up of a randomized clinical trial. J Consult Clin Psychol 2007, 75:187-193.
• [11]Sosna T, Marsenich L: Community Development Team Model: Supporting the Model Adherent Implementation of Programs and Practices. Sacramento: California Institute of Mental Health; 2006. http://www.cimh.org/sites/main/files/file-attachments/cdt_report_0.pdf webcite
• [12]Saldana L, Chamberlain P: Supporting implementation: The role of community development teams to build infrastructure. Am J Community Psychol 2012, 50:334-345.
• [13]Chamberlain P, Brown CH, Saldana L: Observational measure of implementation progress in community based settings: The stages of implementation completion (SIC). Implement Sci 2011, 6:116.
• [14]Saldana L, Chamberlain P, Wang W, Brown CH: Predicting program start-up using the stages of implementation measure. Adm Pol Ment Hlth 2012, 39:419-425.
• [15]Rice ER, Holloway IW, Barman-Adhikari A, Fuentes D, Brown CH, Palinkas LA: A mixed methods approach to network data collection. Field Methods 2013. in press
• [16]Valente TW, Chou CP, Pentz MA: Community coalitions as a system: Effects of network change and adoption of evidence-based substance abuse prevention. Am J Public Health 2007, 97:880-886.
• [17]Valente TW, Pumpuang P: Identifying opinion leaders to promote behavior change. Health Educ Behav 2007, 34:881-896.
• [18]Borgotti SP: NetDraw: Graph Visualization Software. Cambridge, MA: Analytic Technologies; 2002.
• [19]Freeman LC: Visualizing social networks. J Social Structure 2000., 4
• [20]Borgatti SP, Everett MG, Freeman LC: Ucinet for Windows: Software for Social Network Analysis. Cambridge, MA: Analytic Technologies; 2002.
• [21]Freeman LC: Centrality in social networks: I Conceptual clarification. Soc Networks 1979, 1:215-239.
• [22]SAS Institute Inc: SAS/STAT® 9.2. Cary, NC: SAS Institute Inc; 2008.
• [23]Milgram S: The small world problem. Psychology Today 1967, 2(1):60-67.
• [24]Rogers EM: Diffusion of innovations (5th Ed). New York: Free Press; 2003.
• [25]Palinkas LA, Allred CA, Landsverk J: Models of research-operational collaboration for behavioral health in space. Aviat Space Environ Med 2005, 76(Suppl 6):B52-60.
• [26]Brekke JS, Ell K, Palinkas LA: Translational science at the National Institutes of Mental Health: Can Social Work take its rightful place? Res Soc Work Pract 2007, 17:123-133.
• [27]Bandura A: Social foundations of thought and action: A social cognitive theory. Prentice Hall: Englewood Cliffs, NJ; 1986.
• [28]Valente TW: Network models in the diffusion of innovations. Creskill, NJ: Hampton Press; 1995.
• [29]Jung DI, Chow C, Wu A: The role of transformational leadership in enhancing organizational innovation: Hypotheses and some preliminary findings. Leadership Q 2003, 14:525-544.
• [30]Valente TW, Davis RL: Accelerating the diffusion of innovations using opinion leaders. Ann Am Acad Pol Soc Sci 1999, 566:55-67.
• [31]Valente TW, Hoffman BR, Rin-Olson A, Lichtman K, Johnson CA: The effects of a social network method for group assignment strategies on peer led tobacco prevention programs in schools. Am J Public Health 2003, 93:1837-1843.
• [32]Valente TW: Opinion leader intervention in social networks can change HIV risk behavior in high risk communities. Brit Med J 2006, 333:1082-1083.
• [33]Palinkas LA, Fuentes D, Finno M, Garcia AR, Holloway I, Chamberlain P: Inter-organizational collaboration in the implementation of evidence-based practices in public agencies serving abused and neglected youth. Adm Pol Ment Hlth 2012. Aug 11, epub ahead of print, doi:10.1007/s10488-012-0437-5