Deconstructing Health Inequity: A Perceptual Control Theory Perspective



This book offers a radically different perspective on the topic of health inequity. Carey, Tai, and Griffiths use Perceptual Control Theory (PCT) to deconstruct current approaches to understanding, investigating, and addressing problems of health inequity. In the book, the authors propose that health inequity is not a problem per se. Disrupted control, they argue, is the problem that needs to be addressed. From this perspective, research, policy, and health practices directed at addressing health inequity in isolation will offer only partial solutions to the problems created by disrupted control. Addressing problems of disrupted control directly, however, has the potential to entirely resolve issues that are created by health inequity. The authors have extensive clinical and research experience in a wide range of contexts, including: cross-cultural settings; rural, remote, and underserved communities; community mental health settings; prisons; schools; and psychiatric wards. Drawing on these diverse experiences, the authors describe how adopting a Perceptual Control Theory perspective might offer promising new directions for researchers and practitioners who have an interest in addressing issues of inequity and social justice. With a Foreword written by Professor Neil Gilbert this book will provide fresh insights for academics, practitioners, and policymakers in the fields of public health, psychology, social policy, and healthcare.
... It is important to note that SARL is still selfish, it considers the human's outcome only because this is its way to maximize its own outcome. It is interesting to add that it has been shown in field of psychology that people who consider other people's goals and show empathy, feel better themselves and are more likely to reach their own goals [10]. ...
We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the scenario in which one agent is human, while the other is an autonomous agent. We run experiments with human subjects in a simple grid domain, which simulates the single track road problem. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. However, we show that an agent that tries to maximize a linear combination of the human's utility and its own utility, achieves a high score, and significantly outperforms other baselines, including an agent that tries to maximize only its own utility.
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