COVID-19 post-traumatic stress disorder: the role of ACEs, alexithymia, and attachment in the Italian population.
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic is considered a collective traumatic event. Several studies have highlighted high levels of post-traumatic stress disorder (PTSD) symptoms among the general population during the pandemic. The general aim of this research is to explore the role of adverse childhood experiences (ACEs), alexithymia, and anxiety and avoidance attachment dimensions as risk factors that are making individuals more vulnerable to PTSD-COVID-related symptoms. SUBJECTS AND METHODS: The COVID-19-PTSD Questionnaire, 20-Item Toronto Alexithymia Scale (TAS-20), Adverse Childhood Experiences Questionnaire, and the Experiences in Close Relationships-Revised Form (ECR-R) were administered to 224 participants who were between 18 and 65 years of age, and residents of Italy. Socio-demographic variables were also collected. The data was collected between October 2021 and March 2022. RESULTS: The findings of the Spearman correlation analysis showed several significant associations between alexithymia, attachment dimensions, and PTSD symptoms related to COVID-19 diagnosis and age. A multivariable logistic regression model was performed using the COVID-19-PTSD total scores over/under the clinical cut-off as dependent variables and age, gender, anxiety and avoidance attachment scores, ACEs, and total alexithymia as independent variables, with alexithymia total score (B = .071; p = .001), ECR-R Anxiety (B = .034; p = .001) and ECR-R Avoidance (B = -.033; p = .024) showing to respectively increase and reduce the possibility of reporting clinical symptomatology. CONCLUSIONS: Emotional regulation and attachment have been shown to be risk factors for COVID-19 PTSD symptomatology. Focused intervention programs and emotional education can be useful tools for developing protective factors in the general population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".