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Record W3209768373 · doi:10.1002/cpp.2684

Information seeking and health anxiety during the COVID‐19 pandemic: The mediating role of catastrophic cognitions

2021· article· en· W3209768373 on OpenAlexaff
Shreya Jagtap, Amanda L. Shamblaw, Rachel Rumas, Michael W. Best

Bibliographic record

VenueClinical Psychology & Psychotherapy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsAnxietyPsychologyContext (archaeology)PandemicCognitionClinical psychologyCoronavirus disease 2019 (COVID-19)PsychiatryMedicineDisease

Abstract

fetched live from OpenAlex

Cognitive-behavioural models of health anxiety propose a positive association between information seeking and health anxiety; however, it is unclear the extent to which cognitive mechanisms may mediate this relationship. Catastrophic cognitions are one type of cognition that may mediate this relationship, and the COVID-19 pandemic has presented an opportunity to examine these relationships within the context of a global health catastrophe. The current study investigated both cross-sectional (N = 797) and longitudinal (n = 395) relationships between information seeking, health anxiety and catastrophizing during the pandemic. Data were collected using Amazon Mechanical Turk during April and May 2020. Information seeking and health anxiety were positively associated both cross-sectionally and longitudinally (rs = .25-.29). Catastrophic cognitions significantly mediated the relationship between information seeking and health anxiety both cross-sectionally and longitudinally. Developing effective methods of reducing information seeking and catastrophizing may serve to reduce health anxiety during global health crises such as the current pandemic.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.172
GPT teacher head0.508
Teacher spread0.336 · 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

Citations33
Published2021
Admission routes1
Has abstractyes

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