The Predictors of Posttraumatic Stress Symptoms in Recovered COVID-19 Patients: Illness-Related Factors, Depressive and Anxiety Symptoms, Alexithymia and Social Support
Bibliographic record
Abstract
Objective: The COVID-19 pandemic had a profound impact on millions of people, affecting their biological, psychological, and social well-being. The biological effects of SARS-CoV-2 have reportedly been linked to cognitive, emotional, and behavioral symptoms. Traumatic experiences associated with the disease and treatment procedures are regarded as potential risk factors for the emergence of posttraumatic stress symptoms (PTSS); as a result, the severity of the disease and the experience of hospitalization can significantly influence the mental well-being of individuals who have contracted COVID-19. This study aims to assess the factors that influence the development of PTSS among individuals who have recovered from COVID-19. Method: Sociodemographic features, hospitalization status, and physical symptoms of COVID-19 were assessed and PTSS, alexithymia, perceived social support, anxiety, and depression were examined with validated self-report questionnaires (Impacts of Events Scale-Revised, Toronto Alexithymia Scale-20, The Multidimensional Scale of Perceived Social Support, Hospital Anxiety and Depression Scale) in a sample with 105 inpatients and 107 outpatients. Results: PTSS and depression scores of inpatients and outpatients were not significantly different (t(210)=1.246, p=0.214, and t(210)=-0.493, p=0.623, respectively), however, anxiety scores of outpatients were higher (t(209.880)=-2.938, p=0.004). Hospitalization did not significantly affect avoidance and hyperarousal symptoms but was associated with increased intrusion symptoms (t(210)=2.095, p=0.037). In a hierarchical regression model, predictors of PTSS were identified; in step 3 sleep disturbance and initial loss of smell and taste were significant factors (β=0.282, p<0.010; β=0.163, p=0.019, respectively). In step 4, the effects of depression, anxiety, and alexithymia were superior to all other variables (β=0.211, β=0.318, β=0.261, respectively, p<0.010) and initial loss of smell and taste and sleep disturbance did not remain significant in the final model. The final results indicated that psychological assessments made a distinctive contribution to the overall variation in Impacts of Events Scale-Revised scores, extending beyond demographic characteristics, individual variations, and COVID-19 symptoms. Additionally, social support had an indirect impact on PTSS, which was mediated by anxiety, depression, and alexithymia (total bunstd=-0.446, S.E.boot=0.057, CIboot 95% (-0.563, -0.338)). The direct effect of social support on PTSS was non-significant (cunstd=-0.004, S.E.=0.072, CI 95% (-0.146, 0.137)). Conclusion: This study contributes to the literature about the worldwide effects of COVID-19 on mental trauma by establishing the possible predictors for PTSS; and highlights the importance of reflecting on the COVID-19 history of patients and providing appropriate support.
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 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 | 0.001 |
| 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 | 0.000 |
| 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".