A Transdisciplinary Approach in Program Design for Healthcare and Business
Summary:
The transdisciplinary approach means you are deploying multiple theoretical frameworks. The goal is to add layers synergistically to help explain why you need to track certain variables in your evaluation lifecycle, or to help explain why the activities will work. In certain cases, the transdisciplinary approach can help facilitate program fidelity or the workflow processes. In other situations, the transdisciplinary approach can even help with your incremental changes either in the organization you work for or the community you serve as it helps to measure short, medium and long term outcomes.
Program Design
Program design is a continuous lifecycle or portfolio of projects that fit within a strategic goal. This means that, unlike projects, programs do not have an endpoint. This allows for a program lifecycle to receive continuous feedback within deployment and redeployment. By having an evaluation piece in your program design, you can better actualize the results in a reproducible manner. Ultimately, the benefit of designing your program like an actual program is that you get to predict initial outcomes, and then explain how you would make it better with a subject matter SME expert consult.
Subject Matter Experts
SMEs provide domain experience allowing program designers to enhance and optimize their programs to solve complex problems. Remember, generalists are great for a landscape view; however, they do not drill into the land for oil. Having these SMEs is important because they are going to deep dive into the theoretical frameworks that help explain and manipulate outcomes. For example, clinical decision makers may be highly educated people with specific knowledge and experience within a certain domain of healthcare or business.
Case Student 1
You want to design a program that helps prevent and mitigate diabetes regardless of type for your community. You decide to identify the root problem as a lack of healthcare accessibility. The problem definition then becomes “people are experiencing diabetes because they are not attaining annual health exams”.After the problem definition, which is slightly different than the root problem, you create your logic model to influence short, medium and long-term outcomes for the community.
Where does the transdisciplinary thinking enter the framework?
Transdisciplinary thinking comes into the picture when we look to model the logic model either through a SME collaboration or a stop and flow chart. This case study will use a stop and flow chart to better understand the synergistic workflows for using multiple theories simultaneously. If you do not know how to create or use either a logic model or stop and flow chart, then I would recommend purchasing my program on program design.
The stop and flow chart revealed an opportunity to explore information dissemination through social networks, and personal habits. We then planned a meeting with a computer scientist proficient in “r”, and a social scientist familiar with the theory of planned behavior.Because this also includes a digital campaign, we decided to include a User Experience/ Interface designer to help the social scientist better explain our actions to modify the theory of planned behavior framework.
The Transdisciplinary Approach
The transdisciplinary approach is a systems approach to explaining and predicting outcomes qualitatively. Secondly, this approach helps provide direction for the quantitative analysis to help validate your programs through a mixed methods design. A mixed methods design is a fancy way to say that you can validate the outcomes through emotions and numbers. Because you are designing programs to fix problems, you need to be able to validate the findings through prediction and explanation.
Predicting Outcomes
Predicting outcomes is important because it helps justify the work you performed modeling your logic model. Because you had consults with the SMEs, you can now explain why your predictions have a high probability of success. This is called transparency, and transparency is important because it allows you to explain and predict the logic model in hopes to shape the future. Shaping the future with a positive outcome is the goal of the program because it was designed to solve a specific problem.
Explaining Outcomes
Explaining outcomes is just as important as predicting them through probabilistic scores. The benefit to explaining outcomes allows the practitioner to understand the individual variables. Once someone can understand the core elements, they can replace or optimize the elements to better serve the transdisciplinary approach in program design. Not all programs should be cookie cutters because every program in its specific region is going to be different becased of socio-cultural norms. Using the transdisciplinary approach on a high level will reveal the central nuisances involved with cultural specificity.
Optimizing Your Logic Models
Depending on the problem, and your resources you can deploy machine learning techniques to help reduce the time needed for statistical analysis. In the same case study from above, archived data on surveys would help clarify the problem even further. You could notice the difference within the population of the amount of people completing the survey versus those exposed to the survey. This is going to change the problem definition to combating new dangers to your survey collection process such as survey burnout.
Case Study 2
Case study two focused on the same problem definition as problem one, except that archived data illustrated the main culprit being survey burnout. The first question to ask is if we need to keep the same SMEs or replace them in a different consult. In my opinion, we can replace the computer scientist because the people are actively exposed to the survey emphasizing dissemination is not a problem. The data, in my opinion, is emphasizing that the decision to complete the survey is the problem. This means that we need to focus on incentivizing people to complete the survey.
SME Collaboration
Because we want to influence people to overcome their survey burnout, the goal is to work on micro-economics principles such as a nudge and a frame. If we wanted to take one step further for incentivize policies, then we can talk to a policy analyst or consult with the same micro-economist for the nudges and framing. In this meeting, I would include a micro-economist that has experience examining personal decisions based on their socio-economic conditions. Secondly, I would include a social scientist or communications specialist. Remember, the SME consults should directly correlate to the problem definition because we are trying to predict, explain, and optimize our logic models probabilistic success.
The Program Lifecyle
In case study one we did not have archived date to implement machine learning. In case study two, we did have archived data to implement machine learning. In either case, the post-deployment evaluation of our program will allow us more specific data to train our machine learning algorithms to help optimize, predict and explain programs activities. If we wanted to take it a step further, in the second lifecycle there could be an Artificial Intelligence, AI, aspect. This AI can either be weak or strong depending on the programs need to help optimize the employees involved.
The Second Life Cycle
The second lifecycle is going to allow the practitioner to deploy automation techniques. It may not be beneficial to perform automation techniques in the first life cycle because the opportunity to data mine by having a heavy handed relationship with your data may provide critical insight. This insight is going to be an additional layer to helping to explain and predict the outcomes from the model, or help guide the program in its second lifecycle approach. In this lifecycle, there still should be SME consults to help guide the programs second lifecycle in order to better implement its feedback system.
Involving Data Science
You do not need to have a doctoral degree to perform data science. A lot of tools out there have “No code” approaches to your ability to manage and design programs. Now, if you did have the time and capacity to learn automation coding, then your life would be significantly easier. It would be significantly easier because it help optimize your ability to perform by reducing the amount of tasks. It is my recommendation that program designers and managers be made aware of the options to have their data transformed into the cloud.
Program Management Tools
Management tools help track and keep people on the path to success and fidelity. A lot of managers are aware of the typical Gantt Chat, and the use of bar charts among other few data visualization tools. I am not here to speak about these particular tools, but rather dashboards that help you see real-time data. Having the ability to diagnose your programs with real-time data provides you an opportunity to quickly intervene when necessary. This means you do not need to bother people for the data, as you can see it infront of you. By knowing how to manipulate your data, process and outcome evaluation metrics, you can be the isolated manager and the integrated leader simultaneously.
Conclusion
You can be a psilod manager, and an integrated leader simultaneously because of data science. Data science allows you to integrate your transdisciplinary approach as an added layer for prediction and explanation. You do not need to be a data scientist to perform these actions; however, you do need access to consults if you are not the SME within the domain you are working in. If the program is not yet created, then prepare to spend a lot of time on your logic models, and stop and flow charts to deploy transdisciplinary thinking.
Transdisciplinary Approaches to Solving Complex Societal Problems
In a world grappling with multifaceted challenges such as climate change, healthcare crises, and sustainable development, a transdisciplinary approach offers a transformative path forward. By integrating knowledge from different disciplines and engaging with societal actors, this approach enables the development of innovative solutions to real-world problems that no one discipline can solve alone. Transdisciplinary thinking bridges gaps between scientific knowledge, policy-making, and societal needs, empowering decision-makers to address complex societal problems effectively.
Unlike traditional academic disciplines that often operate in isolation, transdisciplinary work fosters collaboration across various disciplines and stakeholders. Through teamwork, knowledge integration, and the inclusion of non-academic stakeholders, transdisciplinary processes aim to generate new knowledge and practical solutions. This holistic method not only advances academic research but also contributes to solving pressing societal challenges, making it a cornerstone for modern problem-solving strategies.
Understanding the Transdisciplinary Approach
What is Transdisciplinary Thinking?
Transdisciplinary thinking is a forward-looking approach that integrates knowledge from different disciplines to address real-world problems and societal issues. Unlike traditional academic silos, this method encourages collaboration across disciplines, fostering a deeper understanding of complex problems. By prioritizing the integration of knowledge systems, transdisciplinary thinking provides a framework for tackling challenges such as climate change, healthcare reform, and sustainable development.
One of the key features of transdisciplinary processes is their ability to bridge gaps between science and policy. Decision-makers, including policymakers and societal actors, often rely on scientific knowledge to guide effective actions. Transdisciplinary thinking ensures that academic research is not only relevant but also accessible, empowering decision-makers to implement strategies informed by diverse perspectives. This approach is particularly valuable in addressing societal problems where the integration of knowledge from both scientific disciplines and real-world stakeholders is essential for impactful solutions.
Transdisciplinary vs. Interdisciplinary Research
While interdisciplinary research focuses on collaboration between academic disciplines, transdisciplinary approaches go a step further by engaging societal actors and non-academic stakeholders. This broader scope facilitates the emergence of transdisciplinarity, where knowledge is co-created by academic and non-academic participants working together. The goal is not only to combine disciplinary knowledge but also to produce new knowledge that directly addresses societal needs.
Interdisciplinary approaches often remain confined to academic research, whereas transdisciplinary research approaches actively involve stakeholders such as community members, industry experts, and policymakers. This collaborative research fosters a shared understanding of complex issues, ensuring that solutions are practical and inclusive. For example, when addressing challenges like sustainability science, transdisciplinary collaboration ensures that different perspectives are considered, from scientific disciplines to grassroots-level stakeholders, creating a more holistic approach to problem-solving.
Applications of Transdisciplinary Collaboration
Tackling Grand Challenges
Transdisciplinary collaboration is particularly effective in tackling grand challenges that require input from multiple sectors and disciplines. Climate change, for instance, is a global issue that intersects with economics, environmental science, social sciences, and public policy. A transdisciplinary approach enables experts from these various disciplines to work together with societal actors to develop sustainable solutions. By integrating specific knowledge from diverse fields, these collaborations address both the scientific and societal dimensions of the problem.
In healthcare systems, transdisciplinary work is instrumental in addressing complex issues such as public health crises and healthcare accessibility. By involving health sciences professionals, policymakers, and community stakeholders, transdisciplinary processes lead to more effective and equitable healthcare solutions. Similarly, education systems benefit from transdisciplinary learning, as educators, researchers, and policymakers collaborate to address challenges like equity, inclusion, and the integration of new learning technologies.
Sustainability science is another area where transdisciplinary approaches shine. By combining knowledge from environmental sciences, social sciences, and economics, these collaborations promote sustainable development initiatives that are not only scientifically sound but also socially acceptable and economically viable. This comprehensive approach ensures that no one aspect of sustainability is overlooked, leading to more balanced and impactful outcomes.
Examples of Transdisciplinary Processes
The manifesto of transdisciplinarity, a foundational document by Basarab Nicolescu, highlights the importance of integrating different forms of knowledge to address societal challenges. This document emphasizes the need for transformation knowledge, which focuses on implementing practical solutions to real-world problems. By adopting this framework, transdisciplinary projects can effectively bridge the gap between theory and practice.
Another key concept in transdisciplinary work is the logic of the included middle. This idea challenges traditional binary thinking and encourages the integration of seemingly contradictory perspectives. For example, in addressing climate change, the logic of the included middle allows scientists, policymakers, and industry leaders to reconcile environmental and economic priorities, fostering solutions that are both sustainable and economically feasible.
Knowledge systems and transformation knowledge play a crucial role in transdisciplinary research. These systems involve the integration of academic and non-academic knowledge, enabling teams to address complex issues with a holistic perspective. For instance, a transdisciplinary project on urban sustainability might combine insights from urban planners, sociologists, environmental scientists, and community leaders to create actionable strategies for sustainable city development.
Key Figures and Concepts in Transdisciplinary Work
Influential Thinkers and Theories
Several key figures have significantly contributed to the development of transdisciplinary research approaches. Hirsch Hadorn and Lang et al, for example, have focused on knowledge integration as a cornerstone of transdisciplinary collaboration. Their work emphasizes the importance of combining disciplinary knowledge with practical insights to address societal problems effectively. This approach not only enhances academic research but also ensures that solutions are grounded in real-world contexts.
Basarab Nicolescu, a prominent advocate of transdisciplinarity, introduced the concept of mode 2 knowledge production. This approach emphasizes the co-creation of knowledge by academic and non-academic stakeholders, breaking away from traditional disciplinary boundaries. Mode 2 knowledge production is particularly relevant in addressing societal challenges, as it prioritizes the integration of diverse perspectives to create practical and innovative solutions.
The National Research Council has also played a pivotal role in advancing transdisciplinary research. By promoting interdisciplinary research and collaborative methodologies, the council has helped establish transdisciplinary processes as essential tools for addressing complex societal challenges. Their contributions to research practice have paved the way for a more integrated and inclusive approach to knowledge creation.
Transdisciplinary Learning in Higher Education
Higher education institutions are increasingly embracing transdisciplinary learning to prepare students for real-world problem-solving. Programs at Utrecht University, for example, focus on equipping students with the skills needed to navigate complex problems through teamwork and collaborative research. Similarly, institutions in New York are incorporating transdisciplinary approaches into their curricula, fostering interdisciplinary work and knowledge integration.
By promoting team science, these programs encourage students to work across various disciplines, developing a shared understanding of societal challenges. This approach not only enhances their academic experience but also prepares them for careers that require systems thinking and collaborative problem-solving. In an era of grand challenges, transdisciplinary learning in higher education is vital for nurturing the next generation of transdisciplinary researchers and decision-makers.
Case Study: Transdisciplinary Research in Action
Designing a Sustainable Healthcare Program
A compelling example of transdisciplinary research in action is the development of a sustainable healthcare program. This initiative begins with problem identification, where team members analyze the multifaceted challenges in healthcare systems, such as accessibility, equity, and sustainability. Using tools like logic models, researchers and practitioners can map out the relationships between available resources, planned activities, and desired outcomes, ensuring a well-structured problem-solving process.
Collaboration with subject matter experts in health sciences is central to this approach. These experts bring specific knowledge that, when combined with insights from policymakers and non-academic stakeholders, ensures that the program addresses both scientific and societal needs. For instance, integrating patient feedback and community perspectives allows the team to design solutions that are both effective and culturally appropriate. Stop and flow charts are often employed to visualize processes and identify potential bottlenecks, streamlining decision-making and implementation.
Integrating Research Methods and Tools
Effective transdisciplinary projects require the use of diverse research methods and tools. Platforms like Google Scholar are invaluable for academic research, providing access to a wealth of scientific knowledge across various disciplines. This enables transdisciplinary researchers to stay updated on the latest findings and incorporate them into their projects. Additionally, leveraging social media platforms helps disseminate information, engage with different stakeholders, and gather real-time feedback from communities involved in the research.
Collaboration with decision-makers and societal actors is another essential component of transdisciplinary work. These stakeholders contribute practical insights that complement academic research, facilitating the integration of knowledge and ensuring that solutions are both actionable and sustainable. For example, in addressing societal problems like urban health disparities, transdisciplinary research teams might work closely with local government officials, healthcare providers, and community leaders to co-create interventions tailored to specific community needs.
The Importance of Transdisciplinary Approaches Today
In an increasingly interconnected world, transdisciplinary approaches are crucial for solving societal problems through collaborative research. These methods foster shared understanding among different stakeholders, ensuring that solutions address the diverse dimensions of complex issues. By promoting knowledge creation and integration, transdisciplinary processes enable researchers to tackle grand challenges like climate change, public health crises, and sustainable development with innovative strategies.
As we face ever-evolving global challenges, the value of transdisciplinary collaboration continues to grow. Whether in academia, healthcare, or public policy, these approaches empower teams to transcend traditional disciplinary boundaries and create impactful solutions. By embracing transdisciplinary work, we can build a more sustainable and equitable future for generations to come.