Impact by Design: Conducting Complex Interventions in Psychology

Intervention and evaluation research in psychology are indispensable tools for creating positive change, informing best practices, and contributing to the ongoing evolution of the field. They bridge the gap between theory and application, transforming psychological knowledge into tangible benefits for individuals and society. It has a strong significance when:

• Improving mental health and wellbeing with effective and cost-effective approaches.
• Addressing societal challenges with targeted individual and community-based solutions.
• Guiding policy and practice to inform policymakers and practitioners about the best evidence-based practices.

But how design, implement and evaluate interventions according to the complexity of the non-theoretical but, real-world?

• During this course, we will dive into a very helpful methodological framework to recognize and adopt appropriate methods to develop and evaluate complex interventions. By reading, discussing, and designing together your own intervention proposal, you will go beyond asking whether an intervention works (by achieving its intended outcome); instead, we'll explore a broader spectrum of questions. This includes identifying additional impacts, assessing its value compared to the resources needed for implementation, theorizing its mechanisms, considering its interaction with the implementation’s context, evaluating its contribution to system 

  change, and understanding how the evidence can be applied to support decision-making in the real world. 

   

  It will allow you to maximize the utility, efficiency, and impact of your own interventional project. The content of this elective course encompasses (among other things):

• Introduction to psychological principles in research.
• The use of quantitative and qualitative data in mixed methods and action-research.
• Theoretical models in the field of behavior change, system thinking and ecological approaches.
• The integration of process evaluations and realist evaluations in complex interventions.
• The use of advance statistics in logic model evaluations.