Correcting malapropisms

strategies to bridge cultural and socioeconomic gaps

Israel Berger*, John A. Cartmill

*Corresponding author for this work

    Research output: Contribution to journalArticle

    Abstract

    Purpose: Although patients and lay people are often more knowledgeable about medical conditions than their predecessors, the dominant culture’s increased involvement in understanding their health and making treatment decisions does not translate into consistently more informed patients. High health literacy is associated with both improved health outcomes and receiving better quality-of-care. Low health literacy disproportionately affects people from marginalized ethnic and language groups. Regardless of how a particular clinician feels about a patient, malapropisms and mis-attributions may cause patients to appear less intelligent or to have lower perceived health literacy, potentially affecting their healthcare experiences with other clinicians. The paper aims to discuss these issues. Design/methodology/approach: This paper discusses the evidence for “up-skilling” patients and uses principles from conversation analysis to demonstrate how malapropisms can be corrected sensitively. Clinician training in skilled communication using the conversation analytic role-play method is also addressed. Findings: Malapropisms are best corrected through modelling rather than calling attention to the error directly, as this allows the patient to save face. Explanations using drawings and clearly written materials may also be useful. Originality/value: Helping patients to improve their communication with clinicians may lead to improved health outcomes through improved quality-of-care.

    Original languageEnglish
    Pages (from-to)84-94
    Number of pages11
    JournalInternational Journal of Human Rights in Healthcare
    Volume10
    Issue number2
    DOIs
    Publication statusPublished - 2017

    Keywords

    • language
    • ethnocentrism
    • healthcare
    • ethnicity
    • health literacy
    • socioeconomic status
    • race
    • conversation analysis

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