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Aversive medical treatments signal a need for support: a mathematical model

Ineffective, aversive and harmful medical treatments are common cross-culturally, historically and today. Using evolutionary game theory, we develop the following model to explain their persistence. Humans are often incapacitated by illness and injury, and are unusually dependent on care from others...

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Detalles Bibliográficos
Autores principales: de Barra, Mícheál, Cownden, Daniel, Jansson, Fredrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427312/
https://www.ncbi.nlm.nih.gov/pubmed/37588405
http://dx.doi.org/10.1017/ehs.2019.2
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author de Barra, Mícheál
Cownden, Daniel
Jansson, Fredrik
author_facet de Barra, Mícheál
Cownden, Daniel
Jansson, Fredrik
author_sort de Barra, Mícheál
collection PubMed
description Ineffective, aversive and harmful medical treatments are common cross-culturally, historically and today. Using evolutionary game theory, we develop the following model to explain their persistence. Humans are often incapacitated by illness and injury, and are unusually dependent on care from others during convalescence. However, such caregiving is vulnerable to exploitation via illness deception, whereby people feign or exaggerate illness in order to gain access to care. Our model demonstrates that aversive treatments can counter-intuitively increase the range of conditions where caregiving is evolutionarily viable, because only individuals who stand to gain substantially from care will accept the treatment. Thus, contemporary and historical “ineffective” treatments may be solutions to the problem of allocating care to people whose true need is difficult to discern.
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spelling pubmed-104273122023-08-16 Aversive medical treatments signal a need for support: a mathematical model de Barra, Mícheál Cownden, Daniel Jansson, Fredrik Evol Hum Sci Research Article Ineffective, aversive and harmful medical treatments are common cross-culturally, historically and today. Using evolutionary game theory, we develop the following model to explain their persistence. Humans are often incapacitated by illness and injury, and are unusually dependent on care from others during convalescence. However, such caregiving is vulnerable to exploitation via illness deception, whereby people feign or exaggerate illness in order to gain access to care. Our model demonstrates that aversive treatments can counter-intuitively increase the range of conditions where caregiving is evolutionarily viable, because only individuals who stand to gain substantially from care will accept the treatment. Thus, contemporary and historical “ineffective” treatments may be solutions to the problem of allocating care to people whose true need is difficult to discern. Cambridge University Press 2019-05-28 /pmc/articles/PMC10427312/ /pubmed/37588405 http://dx.doi.org/10.1017/ehs.2019.2 Text en © The Authors 2019 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
de Barra, Mícheál
Cownden, Daniel
Jansson, Fredrik
Aversive medical treatments signal a need for support: a mathematical model
title Aversive medical treatments signal a need for support: a mathematical model
title_full Aversive medical treatments signal a need for support: a mathematical model
title_fullStr Aversive medical treatments signal a need for support: a mathematical model
title_full_unstemmed Aversive medical treatments signal a need for support: a mathematical model
title_short Aversive medical treatments signal a need for support: a mathematical model
title_sort aversive medical treatments signal a need for support: a mathematical model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427312/
https://www.ncbi.nlm.nih.gov/pubmed/37588405
http://dx.doi.org/10.1017/ehs.2019.2
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