ICD-11 Posttraumatic Stress Disorder and Complex PTSD Among Hospital Medical Workers in China: Impacts of Wenchuan Earthquake Exposure, Workplaces, and Sociodemographic Factors
Bibliographic record
Abstract
BACKGROUND: Previous studies address posttraumatic stress disorder (PTSD) following disasters as a public health issue. However, few studies investigate the long-term effect of disaster exposure on PTSD among hospital medical workers (HMWs). OBJECTIVES: This study aimed to study the prevalence of ICD-11 PTSD and complex PTSD (CPTSD) among exposed and non-exposed HMWs 11 years after the Wenchuan earthquake in China, to identify the factors associated with PTSD and CPTSD scores, and to examine the factor structures of PTSD and CPTSD models. METHODS: A cross-sectional study was conducted using a self-administered online questionnaire. Two thousand fifty-nine valid samples were collected from four hospitals in 2019. Descriptive statistical analysis, multivariate regression models, and confirmatory factor analysis (CFA) were performed. RESULTS: The prevalence of PTSD and CPTSD was 0.58 and 0.34%, respectively. The unexposed group reported higher PTSD and CPTSD scores than the exposed group. The type of workplace and marital status were significantly associated with the PTSD and CPTSD scores of HMWs. The CFA results indicate that both the correlated first-order model and the correlated two-layer model were a good fit to explain the structure of PTSD and CPTSD. CONCLUSION: These findings suggest that few HMWs who were exposed to the Wenchuan earthquake suffered from PTSD or CPTSD 11 years following the disaster. However, psychological support was still necessary for all HMWs, especially for unmarried HMWs who were Working in smaller hospitals. Further research is required to analyze mental health status using ICD-11 PTSD and CPTSD to provide ongoing evidence to help HWMs cope effectively with the challenges of future disasters.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".