Fear of Dying and Catastrophic Thinking Are Associated with More Severe Post-Traumatic Stress Symptoms Following COVID-19 Infection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".