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Record W4413063214 · doi:10.3390/covid5070111

Fear of Dying and Catastrophic Thinking Are Associated with More Severe Post-Traumatic Stress Symptoms Following COVID-19 Infection

2025· article· en· W4413063214 on OpenAlexafffund
Antonina Pavilanis, Lara El-Zein, E. J. LeRoux, Michael Sullivan

Bibliographic record

VenueCOVID · 2025
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsCoronavirus disease 2019 (COVID-19)PsychosocialCausality (physics)MedicineIllness severitySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Psychological interventionClinical psychologyPsychiatrySeverity of illness2019-20 coronavirus outbreakPsychologyDiseaseInternal medicineOutbreakVirology

Abstract

fetched live from OpenAlex

Numerous investigations have revealed elevated rates of post-traumatic stress symptoms (PTSS) following COVID-19 infection. This study examined the relation between illness-related and psychosocial variables in the severity of PTSS in individuals previously infected with COVID-19. The study sample included 381 individuals who had been infected with COVID-19 within the previous 4 months. Participants completed online measures of infection symptom severity, ongoing COVID-19 symptom burden, fear of dying and catastrophic thinking. Age, infection severity, ongoing COVID-19 symptom burden, and fear of dying and catastrophic thinking were significant correlates of the severity of PTSS. Hierarchical regression analysis revealed that age, gender, ongoing COVID-19 symptom burden, fear of dying and catastrophic thinking each made unique significant contributions to the prediction of the severity of PTSS. The results of the present study suggest that fear of dying and catastrophic thinking about COVID-19 symptoms might contribute to the development of PTSS following COVID-19 infection. Interventions aimed at reducing death fears and modifying negative and alarmist appraisals of COVID-19 symptoms might contribute to more positive recovery outcomes in individuals who are infected with COVID-19. The cross-sectional design of this study precludes statements about causality, and conclusions about temporal relations among variables must await replication in a longitudinal design.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.369
Teacher spread0.329 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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