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Using Twitter to assess attitudes to schizophrenia and psychosis

AIMS AND METHOD: Schizophrenia is a psychotic disorder that is stereotypically stigmatised as untreatable and associated with violence. Several authorities have suggested that changing the name, for example to psychosis, would reduce such stigmatisation. We aimed to compare attitudes to schizophreni...

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Autores principales: Passerello, Giorgianna L., Hazelwood, James E., Lawrie, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642990/
https://www.ncbi.nlm.nih.gov/pubmed/30784393
http://dx.doi.org/10.1192/bjb.2018.115
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author Passerello, Giorgianna L.
Hazelwood, James E.
Lawrie, Stephen
author_facet Passerello, Giorgianna L.
Hazelwood, James E.
Lawrie, Stephen
author_sort Passerello, Giorgianna L.
collection PubMed
description AIMS AND METHOD: Schizophrenia is a psychotic disorder that is stereotypically stigmatised as untreatable and associated with violence. Several authorities have suggested that changing the name, for example to psychosis, would reduce such stigmatisation. We aimed to compare attitudes to schizophrenia and psychosis on Twitter to see if psychosis was associated with less negative attitudes. Tweets containing the terms ‘schizophrenia’, ‘schizophrenic’, ‘psychosis’ or ‘psychotic’ were collected on www.twitter.com and were captured with NCapture. On NVivo, tweets were coded into categories based on user type, tweet content, attitude and stigma type by two independent raters. We compared the content and attitudes of tweets referring to schizophrenia/schizophrenic and psychosis/psychotic. RESULTS: A total of 1120 tweets referring to schizophrenia/schizophrenic and 1080 referring to psychosis/psychotic were identified over two 7-day periods; 424 original tweets for schizophrenia and 416 original tweets for psychosis were included in the analysis. Psychosis was significantly more commonly included in tweets expressing negative attitudes (n=131, 31.5%) than schizophrenia (n=41, 9.7%) (χ² = 237.03, P < 0.0001). Of the personal opinions or dyadic interactions, 125 (53.4%) in the psychosis data set were stigmatising, compared with 33 (24.6%) of those in the schizophrenia set (χ² = 44.65, P < 0.0001). CLINICAL IMPLICATIONS: The terms psychosis/psychotic are associated with a significantly higher number of tweets with negative content than schizophrenia/schizophrenic. Together with other evidence, this suggests that changing the name of schizophrenia to psychosis will not reduce negative attitudes toward the condition. DECLARATION OF INTEREST: S.L. has received personal fees from Otsuka and Sunovion, and personal and research fees from Janssen.
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spelling pubmed-66429902019-08-01 Using Twitter to assess attitudes to schizophrenia and psychosis Passerello, Giorgianna L. Hazelwood, James E. Lawrie, Stephen BJPsych Bull Original Papers AIMS AND METHOD: Schizophrenia is a psychotic disorder that is stereotypically stigmatised as untreatable and associated with violence. Several authorities have suggested that changing the name, for example to psychosis, would reduce such stigmatisation. We aimed to compare attitudes to schizophrenia and psychosis on Twitter to see if psychosis was associated with less negative attitudes. Tweets containing the terms ‘schizophrenia’, ‘schizophrenic’, ‘psychosis’ or ‘psychotic’ were collected on www.twitter.com and were captured with NCapture. On NVivo, tweets were coded into categories based on user type, tweet content, attitude and stigma type by two independent raters. We compared the content and attitudes of tweets referring to schizophrenia/schizophrenic and psychosis/psychotic. RESULTS: A total of 1120 tweets referring to schizophrenia/schizophrenic and 1080 referring to psychosis/psychotic were identified over two 7-day periods; 424 original tweets for schizophrenia and 416 original tweets for psychosis were included in the analysis. Psychosis was significantly more commonly included in tweets expressing negative attitudes (n=131, 31.5%) than schizophrenia (n=41, 9.7%) (χ² = 237.03, P < 0.0001). Of the personal opinions or dyadic interactions, 125 (53.4%) in the psychosis data set were stigmatising, compared with 33 (24.6%) of those in the schizophrenia set (χ² = 44.65, P < 0.0001). CLINICAL IMPLICATIONS: The terms psychosis/psychotic are associated with a significantly higher number of tweets with negative content than schizophrenia/schizophrenic. Together with other evidence, this suggests that changing the name of schizophrenia to psychosis will not reduce negative attitudes toward the condition. DECLARATION OF INTEREST: S.L. has received personal fees from Otsuka and Sunovion, and personal and research fees from Janssen. Cambridge University Press 2019-08 /pmc/articles/PMC6642990/ /pubmed/30784393 http://dx.doi.org/10.1192/bjb.2018.115 Text en © The Authors 2019 http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
spellingShingle Original Papers
Passerello, Giorgianna L.
Hazelwood, James E.
Lawrie, Stephen
Using Twitter to assess attitudes to schizophrenia and psychosis
title Using Twitter to assess attitudes to schizophrenia and psychosis
title_full Using Twitter to assess attitudes to schizophrenia and psychosis
title_fullStr Using Twitter to assess attitudes to schizophrenia and psychosis
title_full_unstemmed Using Twitter to assess attitudes to schizophrenia and psychosis
title_short Using Twitter to assess attitudes to schizophrenia and psychosis
title_sort using twitter to assess attitudes to schizophrenia and psychosis
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642990/
https://www.ncbi.nlm.nih.gov/pubmed/30784393
http://dx.doi.org/10.1192/bjb.2018.115
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