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Table 2 Integration of quantitative and qualitative findings of a network analysis perspective to the implementation of EIDM in public health organizations

From: Informing the implementation of evidence-informed decision making interventions using a social network analysis perspective; a mixed-methods study

Quantitative highlights Qualitative themes Integration
Engagement in EIDM training
• Health units A and C had higher engagement rate (8% of the staff in unit A, 1% in unit B, and 9% in units C).
• The engagement rate of central actors in unit A, B, C was 61%, 10%, and 56% respectively.
• At unit A, most of the engaged staff were managers and project specialists.
• At health unit B, most of the engaged staff were health promotion consultants, most of whom were not central actors.
• At health unit C half of the central actors were epidemiologists who mostly did not engage in the intervention.
• Central network actors had higher baseline EIDM behavior scores than others.
• Organizational leaders at units A and C strongly promoted the intervention
• Especially at unit A, the leaders actively monitored the progress, and controlled the quality of the output
• The main mechanism of choosing staff to participate in trainings was the relevance of their roles to EIDM and the health problem.
• The staff generally did not have given much choice at time of recruitment, and were not optimally informed about the value of the study and the importance and consequences of their participation.
• The relevance of health promotion consultants’ role to EIDM at unit B was not clear for some staff, which resulted in negative reactions.
• Epidemiologists at unit C did not engage in the intervention because they were not assigned to programs, and did not believe EIDM was relevant
Leadership support: Staff are more likely to adopt EIDM if organizational leaders strongly support it and directly engage in the process
Positional compatibility: Staff are more likely to adopt EIDM if its relevance to their formal roles is clear
Participatory engagement: Staff are more likely to adopt EIDM if they are clearly informed about the training processes and expectations, and feel in control over participation
Networking and communication
• Only at unit A, the KB was identified as a central staff. Even though she was not a formal employee
• In three health units highly engaged staff showed a tendency to form clusters.
• KB was the main deliverer of the intervention. (Especially at unit A)
• Librarians, if get engaged, supported the EIDM process
• Co-participation in workshops and working on the same evidence summary provided the staff with an opportunity to share their concerns and progress with their peers and shape new social ties, if they were sustained by regular communications (progress meetings)
Support networks: Sharing experiences and concerns in regularly scheduled meetings of EIDM trainees facilitate the development of an atmosphere of trust among engaged staff.
EIDM champions: the KB and librarian are main motivators and deliverers of EIDM training and support. Their professional competence, social engagement, and physical accessibility affect implementation success
Recognition
• At unit A, highly engaged staff became more popular
• Staff with higher baseline and higher improvement in EIDM behavior scores became more popular
• Network became more centralized around already central staff
• Some of the highly engaged staff became widely popular after presenting their findings in department-wise events, being promoted by the leaders, and word of mouth
• At unit A (where engagement in the intervention resulted in a considerable prestige effect) the staff who were not chosen responded negatively to the unequal carrier promotion opportunities and the ‘ivory tower’ position of project specialists
Recognition and promotion: Trained staff become more central in networks if they have the opportunity to be recognized as experts in EIDM through presentations at organization-wide events and endorsement by organizational leaders.
Positional advantage: The positional advantage of central network actors through the selective training interventions results in a “rich get richer” pattern. Selective training, on the other hand, may result in negative reactions by the staff who were not chosen.