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Record W3201268909 · doi:10.2196/28239

Pandemic Information Dissemination and Its Associations With the Symptoms of Mental Distress During the COVID-19 Pandemic: Cross-sectional Study

2021· article· en· W3201268909 on OpenAlexvenueno aff
Ole Myklebust Amundsen, Asle Hoffart, Sverre Urnes Johnson, Omid V. Ebrahimi

Bibliographic record

VenueJMIR Formative Research · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyPandemicMental healthCross-sectional studyDistressPopulationPsychopathologyDepression (economics)PsychologyMedicineClinical psychologyPublic healthEnvironmental healthPsychiatryDemographyCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

BACKGROUND: The 2020-2021 COVID-19 pandemic has added to the mental health strain on individuals and groups across the world in a variety of ways. Viral mitigation protocols and viral spread affect people on all continents every day, but at widely different degrees. To understand more about the mental health consequences of the pandemic, it is important to investigate whether or how people gather pandemic-related information and how obtaining this information differentially affects individuals. OBJECTIVE: This study aimed to investigate whether and to what extent higher levels of COVID-19-related media consumption across information sources are associated with the symptoms of anxiety, health anxiety, and depression, and whether and to what extent using social media and online interactive platforms versus traditional media platforms is associated with the symptoms of anxiety, health anxiety, and depression. Additionally, we aimed to investigate whether and to what extent avoidance of COVID-19-related information is associated with the aforementioned symptoms. METHODS: In a cross-sectional preregistered survey, 4936 participants responded between June 22 and July 13, 2020. Eligible participants were adults currently residing in Norway and were thus subjected to identical viral mitigation protocols. This sample was representative of the Norwegian population after utilizing an iterative raking algorithm to conduct poststratification. As 2 subgroups (transgender and intersex individuals) were too small to be analyzed, the final sample for descriptive statistics and regressions included 4921 participants. Multiple regressions were used to investigate associations between the symptoms of psychopathology and COVID-19-related information dissemination. Part correlations were calculated as measures of the effect size for each predictor variable. Due to the large anticipated sample size, the preregistered criterion for significance was set at P<.01. RESULTS: The symptoms of anxiety and health anxiety were significantly associated with obtaining information from newspapers (P<.001), social media (P<.001), and the broader categories of online interactive (P<.001) and traditional media (P<.001). The symptoms of depression were significantly associated with obtaining information from newspapers (P=.003), social media (P=.009), and the broader category of online interactive media (P<.001). Additionally, avoidance of COVID-19-related information showed a significant association in all 3 domains of psychopathological symptoms (anxiety and depression, P<.001; health anxiety, P=.007). CONCLUSIONS: This study found significant associations between the symptoms of psychopathology and the use of media for obtaining information related to the COVID-19 pandemic. Significant findings for obtaining information through newspapers, social media, and online interactive media were seen across all 3 measures of psychopathology. Avoidance of COVID-19-related information and associations with the symptoms of psychopathology emerged as core findings, with generally higher effect sizes compared with information attainment. TRIAL REGISTRATION: ClinicalTrials.gov NCT04442360; https://clinicaltrials.gov/ct2/show/NCT04442360.

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 score0.053
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.527
Teacher spread0.419 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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