Grower networks support adoption of innovations in pollination management: The roles of social learning, technical learning, and personal experience.

Management decisions underpinning availability of ecosystem services and the organisms that provide them in agroecosystems, such as pollinators and pollination services, have emerged as a foremost consideration for both conservation and crop production goals. There is growing evidence that innovative management practices can support diverse pollinators and increase crop pollination. However, there is also considerable debate regarding factors that support adoption of these innovative practices. This study investigated pollination management practices and related knowledge systems in a major crop producing region of southwest Michigan in the United States, where 367 growers were surveyed to evaluate adoption of three innovative practices that are at various stages of adoption. The goals of this quantitative, social survey were to investigate grower experience with concerns and benefits associated with each practice, as well as the influence of grower networks, which are comprised of contacts that reflect potential pathways for social and technical learning. The results demonstrated that 17% of growers adopted combinations of bees (e.g. honey bees, Apis mellifera, with other species), representing an innovation in use by early adopters; 49% of growers adopted flowering cover crops, an innovation in use by the early majority 55% of growers retained permanent habitat for pollinators, an innovation in use by the late majority. Not all growers adopted innovative practices. We found that growers' personal experience with potential benefits and concerns related to the management practices had significant positive and negative relationships, respectively, with adoption of all three innovations. The influence of these communication links likely has different levels of importance, depending on the stage of the adoption that a practice is experiencing in the agricultural community. Social learning was positively associated with adopting the use of combinations of bees, highlighting the potentially critical roles of peer-to-peer networks and social learning in supporting early stages of adoption of innovations. Engaging with grower networks and understanding grower experience with benefits and concerns associated with innovative practices is needed to inform outreach, extension, and policy efforts designed to stimulate management innovations in agroecosystems.

Hypothesis generation using network structures on community health center cancer-screening performance.

RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers.

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman's Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability.