Evidence map›Paper›PMID 36941953›Full record

ArticleSociological research online2021

Stigma Mutation: Tracking Lineage, Variation and Strength in Emerging COVID-19 Stigma.

Hannah Farrimond

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Article in Sociological research online, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Hannah FarrimondUniversity of Exeter, UK.ORCID https://orcid.org/0000-0001-9946-8620

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, I propose a novel theoretical framework for conceptualizing pandemic stigma using the metaphor of 'mutation'. This metaphor highlights that stigma is not a static or fixed state but is enacted through processes of continuity and change. The following three orienting concepts are identified: (a) lineage (i.e. origin narratives and initial manifestations are created in relation to existing stigmas, stereotypes, and outgroups), (b) variation (i.e. stigma changes over time in response to new content and contexts), and (c) strength (i.e. stigma can be amplified or weakened through counter- or de-stigmatizing forces). I go on to use this metaphor to offer an analysis of the emergence of COVID-19 stigma. The lineage of COVID-19 stigma includes a long history of contagious disease, resonant with fears of contamination and death. Origin narratives have stigmatized Asian/Chinese groups as virus carriers, leading to socio-political manifestations of discrimination. Newer 'risky' groups have emerged in relation to old age, race and ethnicity, poverty, and weight, whose designation as 'vulnerable' simultaneously identifies them as victims in need of protection but also as a risk to the social body. Counter-stigmatizing trends are also visible. Public disclosure of having COVID-19 by high-status individuals such as the actor Tom Hanks has, in some instances, converted 'testing positive' into shared rather than shamed behaviour in the West. As discourses concerning risk, controllability, and blame unfold, so COVID-19 stigma will further mutate. In conclusion, the metaphor of mutation, and its three concepts of lineage, variation, and strength, offers a vocabulary through which to articulate emergent and ongoing stigma processes. Furthermore, the concept of stigma mutation identifies a clear role for social scientists and public health in terms of process engagement; to disrupt stigma, remaking it in less deadly forms or even to prevent its emergence altogether.

Indexed as

anti-AsianChinaCOVID-19discriminationprejudiceprocesspublic healthstereotypesstigma

Identifiers

PMID36941953
PMCPMC10008726

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.