Self-Medication: A Bayesian Approach Proposal
Palabras clave:
Bayesian Model, Self-Medication, Pharmacovigilance, Cognition, EquationResumen
The article examines self-medication from a multidisciplinary perspective, specifically through the lens of Bayesian statistics, a data analysis and parameter estimation method grounded in Bayes’ theorem. It develops a general equation proposal based on this theorem. The text highlights how human judgments can be interpreted through a probabilistic model, leveraging theoretical approaches to Bayesian models of cognition (Griffiths, Chater, and Tenenbaum, 2024). It underscores the complexity of the self-medication phenomenon, which has been studied through diverse theoretical frameworks, including economic paradigms, process-based approaches, and transgression models. The ability to predict medication effects in relation to current symptoms is pivotal, as it allows for risk anticipation and outcome forecasting, emphasizing the significance of disciplinary and methodological pluralism in the realm of pharmacovigilance. The article advocates for the necessity of real-time monitoring to identify emerging concerns about drug safety. Furthermore, it offers an epistemological framework and an approach that integrates probabilistic inference with the practice of self-medication, underscoring the critical roles of education and vigilance in this domain.
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