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Abstract
This paper explores the question of what systems thinking adds to the field of global health. Observing that elements of systems thinking are already common in public health research, the article discusses which of the large body of theories, methods, and tools associated with systems thinking are more useful. The paper reviews the origins of systems thinking, describing a range of the theories, methods, and tools. A common thread is the idea that the behavior of systems is governed by common principles that can be discovered and expressed. They each address problems of complexity, which is a frequent challenge in global health. The different methods and tools are suited to different types of inquiry and involve both qualitative and quantitative techniques. The paper concludes by emphasizing that explicit models used in systems thinking provide new opportunities to understand and continuously test and revise our understanding of the nature of things, including how to intervene to improve people’s health.
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Background
In the rapidly changing field of global health, it is hard to know whether the recent attention to systems thinking is just another fad, or something more durable that offers usable insights for understanding and action. Some see systems thinking as providing a powerful language to communicate and investigate complex issues, while others are confused by the sizable and amorphous body of theories, methods, and tools involved. Time will tell, of course, but in the meantime, it is helpful to consider why we would use systems thinking in a field that already draws upon a rich collection of theories, methods, and tools from the health sciences, social sciences, engineering, mathematics, and other disciplines.
From mental models to explicit ones
At its core, systems thinking is an enterprise aimed at seeing how things are connected to each other within some notion of a whole entity. We often make connections when conducting and interpreting research, or in our professional practice when we make an intervention with an expectation of a result. Anytime we talk about how some event will turn out, whether the event is an epidemic, a war, or other social, biological, or physical process, we are invoking some mental model about how things fit together. However, rather than relying on implicit models, with hidden assumptions and no clear link to data, systems thinking deploys explicit models, with assumptions laid out that can be calibrated to data and repeated by others. The word system is derived from the Greek sunistánai, meaning “to cause to stand together.” If we consider that a system is a perceived whole, made up of parts that interact toward a common purpose, we recognize that the ability to perceive, and the quality of that perception, is also part of what causes a system to stand together. Systems thinking is intended to improve the quality of those perceptions of the whole, its parts, and the interactions within and between levels.
Every interpretation of a research result involves a model, whether it is a physical model used for experimentation, a statistical model used to estimate the relationships between variables, or a conceptual model about how elements are connected. A model is simply a way we compactly represent and understand an object, phenomenon, or system. As much as research involves observation and experimentation, I would argue that good research is also about building and using explicit models rather than implicit ones. The real question is not whether we should be using systems thinking, as broadly described here, but rather, which of the many theories, methods, and tools currently associated with the field of systems thinking are most useful in particular settings.
For example, where individual people interact directly with one another (e.g., transmitting
06/29/22 9:11 AM
