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How a Process-Based Idionomic Approach Changes our Understanding of Mindfulness as a Method and Process

PsyArXiv February 19, 2024 preprint DOI: 10.31234/osf.io/xkcs6 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or philosophical paper
Topics Meditation
Key points Argues that mindfulness research needs to shift from general, linear hypotheses to network models of process relations, because average processes and average networks cannot be reliably applied to individuals without ergodicity. Contends that incorporating mindfulness into evidence-based care requires wiser, safer deployment and bottom-up methodological changes that measure and model each unique individual.

Abstract

Mindfulness research began with broad hypotheses such as “Mindfulness reduces stress” or “Mindfulness drives well-being.” As research has evolved, we as a field have begun to think more in terms of individual heterogeneity, but the hypotheses still appear to be linear and fairly general, such as “more mindfulness will lead to more well-being for some.” As our process focus becomes more nuanced, we need to think in terms of networks of process relations. That kind of network thinking is progressive, but it cannot be reliably done purely nomothetically. Average processes are not processes at all, and average networks do not escape the requirement of ergodicity to be applied with confidence to individuals. We will need a more bottom-up change in our measures and research methods to truly understand how mindfulness skills impact people’s lives. Incorporating mindfulness skills into evidence-based care is a positive thing, but how we do it differs from the spiritual and religious traditions that have developed over thousands of years. As empirical clinicians, we need to learn to be wiser and to deploy these skills in a way that makes them safe for individuals and for cultures. Every individual matters in such a journey, and so skills and aspirations of the unique individuals we work with must be measured, modeled, and understood in a way that allows their unique voices to be heard.

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