Evidence map›Paper›PMID 37023096›Full record

ArticlePloS one2023

How does the public understand the causes of mental disorders? An analysis of Irish news media before and during the COVID-19 pandemic.

Leigh Huggard, Cliódhna O'Connor

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Leigh HuggardSchool of Psychology, University College Dublin, Belfield, Dublin 4, Ireland.ORCID 0000-0001-8346-4182
Cliódhna O'ConnorSchool of Psychology, University College Dublin, Belfield, Dublin 4, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public perceptions of the determinants of mental illness have important implications for attitudes and stigma, but minimal previous research has explored how causal attributions are spontaneously invoked in everyday public discourse. This study investigated how causal explanations for mental illness are disseminated in popular Irish news media, in the two years before and after the onset of the COVID-19 pandemic. Keyword searches of a news media database identified 1,892 articles published between March 2018 to March 2022 that mentioned one of six categories of mental disorders: anxiety disorders, mood disorders, substance-related disorders, personality disorders, eating disorders, and psychotic disorders. Overall, 25% of the identified articles contained a causal explanation for mental illness. Inductive content analysis revealed the content and prevalence of eight types of causal explanations for mental disorders. Overall, attributions to life events/experiences, the cultural/societal environment, interpersonal relations, and health and lifestyle factors occurred more frequently than attributions to biological or psychological determinants. Life events/experiences were the most common explanation for anxiety and personality disorders, cultural/societal environment for eating disorders, and health/lifestyle factors for mood and psychotic disorders. Interpersonal factors in mental illness aetiology became more salient following the COVID-19 pandemic. The findings emphasise the need for theory and research on lay explanations of mental disorders to account for diversity, both in the range of attributions invoked, and in how attributional patterns shift across time and mental disorders category.

Indexed as

COVID-19Mental DisordersPsychotic DisordersAnxiety DisordersHumansPandemics

Identifiers

PMID37023096
PMCPMC10079019

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.