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Record W4366067923 · doi:10.2196/42707

COVID-19 Infodemic and Impacts on the Mental Health of Older People: Cross-sectional Multicenter Survey Study

2023· article· en· W4366067923 on OpenAlexvenueno aff
Patrícia Rodrigues Braz, Tiago Ricardo Moreira, Andréia Queiróz Ribeiro, Luciane Ribeiro, Fábio da Costa Carbogim, Vilanice Alves de Araújo Püschel, Jack Roberto Silva Fhon, Eduarda Rezende Freitas, Ione Carvalho Pinto, Fabiana Costa Machado Zacharias, Gylce Eloisa Cabreira Panitz Cruz, Richardson Miranda Machado, Rosimere Ferreira Santana, Priscilla Alfradique de Souza, Graziele Ribeiro Bitencourt, Alexandre Fávero Bulgarelli, Ricardo Bezerra Cavalcante

Bibliographic record

VenueJMIR Aging · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersUniversidade Federal de Juiz de ForaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMisinformationMental healthCross-sectional studyPandemicSocial mediaPublic healthAnxietyLonelinessPsychologyCoronavirus disease 2019 (COVID-19)MedicineGerontologyEnvironmental healthPsychiatryNursingPolitical scienceDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic received widespread media coverage due to its novelty, an early lack of data, and the rapid rise in deaths and cases. This excessive coverage created a secondary "infodemic" that was considered to be a serious public and mental health problem by the World Health Organization and the international scientific community. The infodemic particularly affected older individuals, specifically those who are vulnerable to misinformation due to political positions, low interpretive and critical analysis capacity, and limited technical-scientific knowledge. Thus, it is important to understand older people's reaction to COVID-19 information disseminated by the media and the effect on their lives and mental health. OBJECTIVE: We aimed to describe the profile of exposure to COVID-19 information among older Brazilian individuals and the impact on their mental health, perceived stress, and the presence of generalized anxiety disorder (GAD). METHODS: This cross-sectional, exploratory study surveyed 3307 older Brazilians via the web, social networks, and email between July 2020 and March 2021. Descriptive analysis and bivariate analysis were performed to estimate associations of interest. RESULTS: Major proportions of the 3307 participants were aged 60 to 64 years (n=1285, 38.9%), female (n=2250, 68.4%), and married (n=1835, 55.5%) and self-identified as White (n=2364, 71.5%). Only 295 (8.9%) had never started or completed a basic education. COVID-19 information was mainly accessed on television (n=2680, 81.1%) and social networks (n=1943, 58.8%). Television exposure was ≥3 hours in 1301 (39.3%) participants, social network use was 2 to 5 hours in 1084 (32.8%) participants, and radio exposure was ≥1 hour in 1223 (37%) participants. Frequency of exposure to social networks was significantly associated with perceived stress (P=.04) and GAD (P=.01). A Bonferroni post hoc test revealed significantly different perceived stress in participants who were exposed to social networks for 1 hour (P=.04) and those who had no exposure (P=.04). A crude linear regression showed that "some" social media use (P=.02) and 1 hour of exposure to social media (P<.001) were associated with perceived stress. Adjusting for sociodemographic variables revealed no associations with this outcome variable. In a crude logistic regression, some social media use (P<.001) and 2 to 5 hours of exposure to social media (P=.03) were associated with GAD. Adjusting for the indicated variables showed that some social network use (P<.001) and 1 hour (P=.04) and 2 to 5 hours (P=.03) of exposure to social media were associated with GAD. CONCLUSIONS: Older people, especially women, were often exposed to COVID-19-related information through television and social networks; this affected their mental health, specifically GAD and stress. Thus, the impact of the infodemic should be considered during anamnesis for older people, so that they can share their feelings about it and receive appropriate psychosocial care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.506
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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