The Predictors of Posttraumatic Stress Symptoms in Recovered COVID-19 Patients: Illness-Related Factors, Depressive and Anxiety Symptoms, Alexithymia and Social Support
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».