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Gendered dimension of chronic pain patients with low and middle income: A text mining analysis

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  • Ana M Peiró
  • Patricia Carracedo
  • Laura Agulló
  • Sónia F Bernardes

Abstract

Methods: This is a mixed-method study using individual interviews (duration between 40–60 minutes) of 181 CNCP patients (71% females) in a tertiary Pain Care Unit, and applying the text mining methodology. Incomes (low or middle) and gender roles (productive vs. reproductive)”. Results: Gender differences were identified in the words used to describe pain impact in working and social life, domestic responsibilities, and family relationships. Albeit having similar CNCP severity and interference, women were on average 8 years older, compared to men, with longer referral time from Primary Care, less retired but more homemakers, showing a greater impact on their mental health. The most discriminating word explaining pain impact for CNCP women was “husband”, for men was “work”, especially among middle income groups. The way men, with a productive gender role, talked about the impact of CNCP in their lives stressed the word “work”. In contrast, men with reproductive roles stressed the words “chores, family or limited” as women with low-income did. Only low-income men used the word “help”. The text mining analysis indicates a discrepant distribution of men and women into traditional gender social roles that are consistent with stereotypical traits and may have an impact on pain care. There is a need of an intersectional perspective as part of pain assessment, to develop novel self-management interventions for men and women.

Suggested Citation

  • Ana M Peiró & Patricia Carracedo & Laura Agulló & Sónia F Bernardes, 2024. "Gendered dimension of chronic pain patients with low and middle income: A text mining analysis," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-12, December.
  • Handle: RePEc:plo:pone00:0311292
    DOI: 10.1371/journal.pone.0311292
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    References listed on IDEAS

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    1. Werner, Anne & Malterud, Kirsti, 2003. "It is hard work behaving as a credible patient: encounters between women with chronic pain and their doctors," Social Science & Medicine, Elsevier, vol. 57(8), pages 1409-1419, October.
    2. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
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