Karin Ingold’s post explains the role of scientific knowledge brokering in coalitions in Integration and Implementation Insights https://i2insights.org/ In Ottawa, we have had various examples of this, be it the Alliance to End Homelessness or harm reduction networks. I find it useful to reflect on Ingold’s point that suggests that the loss of neutrality in Adversarial advocacy results in, ” no possibility for knowledge brokerage exists.” and need to become “non neutral actors.”
What roles can science and scientific experts adopt in policymaking? One way of examining this is through the Advocacy Coalition Framework (Sabatier and Jenkins-Smith 1993). This framework highlights that policymaking and the negotiations regarding a political issue—such as reform of the health system, or the introduction of an energy tax on fossil fuels—is dominated by advocacy coalitions in opposition. Advocacy coalitions are groups of actors sharing the same opinion about how a policy should be designed and implemented. Each coalition has its own beliefs and ideologies and each wants to see its preferences translated into policies.
Health Evidence www.healthevidence.org shares tools that guide practice evidence, developed in collaboration with local public health organizations. While targeted at public health some of the tools provide useful approaches for emerging front line projects.
Looking for tools to help you find and use research evidence? Use the Health Evidence™ practice tools to help you work through the evidence-informed decision making process; search for evidence, track your search, and share lessons learned with your public health organization.
Example of tools:
- Evidence-Informed Decision Making (EIDM) Checklist
- Developing an Efficient Search Strategy Using PICO
- Levels & Sources of Public Health Evidence
- Resources to Guide & Track Your Search
- Keeping Track of Search Results: A Flowchart
- Briefing Note: Decisions, Rationale and Key Findings Summary
- Improving Future Decisions: Optimizing the Decision Process from Lessons Learned
See the current tools at their site here: http://www.healthevidence.org/practice-tools.aspx
From the journal of Implementation Science, https://implementationscience.biomedcentral.com/articles/10.1186/s13012-017-0607-7
…In this paper, we propose the use of architectural frameworks to develop LHSs that adhere to a recognized vision while being adapted to their specific organizational context. Architectural frameworks are high-level descriptions of an organization as a system; they capture the structure of its main components at varied levels, the interrelationships among these components, and the principles that guide their evolution.
This paper shared as one of the resources was found by Vicky Ward https://kmbresearcher.wordpress.com/, who was at the Canadian Knowledge Mobilization Forum, http://www.knowledgemobilization.net/event/2017-canadian-knowledge-mobilization-forum/
PUT SIMPLY: According to an intersectionality perspective, inequities are never the result of single, distinct factors. Rather, they are the outcome of intersections of different social locations, power relations and experiences.
paper by Olena Hankivsky, PhD of https://www.sfu.ca/iirp/
see the paper here: https://www.sfu.ca/iirp/documents/resources/101_Final.pdf
A 10 minute talk with Catherine D’Ignazio on http://www.cbc.ca/radio/spark, worth a listen,— in order to co-construct Data collection, to represent the limitations of data, being willing to visualize what is NOT THERE
Picture by (flickr CC/Ewan Munro)
We live in an era of unequalled amounts of data. The Big Data age. And the sheer volume of it can be overwhelming. But no worries, you can look at a data visualization, and it will clear it all up. There’s a graph, or a chart or an infographic! Each an objective distillation of reality, right there in pictures. Only, what if those visualizations don’t tell the whole truth?
Catherine D’Ignazio is an Assistant Professor of Data Visualization and Civic Media in the Journalism Department at Emerson College in Boston. She thinks that we tend to accept data visualizations as facts because they seem to present an expert and neutral point of view.
But Catherine says that the perspectives of groups like women, minorities and others can often be excluded from what we consider objective data about the world around us. Which is why, earlier this year when this episode first aired, she posed the question, what would feminist data visualization look like?
The Interview is here: http://www.cbc.ca/radio/spark/307-snitching-stealing-exclusions-and-more-1.3414341/who-do-data-visualizations-leave-out-1.3414350?utm_content=buffere6850&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer