A Cross-Sectional Study on Posttraumatic Stress Disorder among Elderly Qiang Citizens 3 Years after the Wenchuan Earthquake in China
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of posttraumatic stress disorder (PTSD) and to identify the associated risk factors among elderly citizens belonging to the Qiang ethnic minority group 3 years after the Wenchuan earthquake in China. METHODS: A cross-sectional survey of 287 respondents aged 60 years and older was conducted to collect data in Beichuan County, a heavily damaged area. PTSD was assessed according to the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Independent demographic, socioeconomic and trauma exposure variables were also measured. Association between the independent variables and PTSD was analyzed using logistic regression analysis. RESULTS: The prevalence of PTSD was 22.65% among elderly Qiang citizens in Beichuan County. Being female, being widowed, having a low level of education, having low monthly income, suffering bodily injury, being bereaved, and having a low level of social support were risk factors significantly related to the development of PTSD. CONCLUSION: The results indicate that PTSD remained at an elevated level among elderly Qiang citizens in the heavily damaged area 3 years after the Wenchuan earthquake. Effective and sustainable mental health services are needed and should be directed particularly to the elderly Qiang citizens who are among the groups most vulnerable to the direct impact of the earthquake.
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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 | 0.001 |
| 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 | 0.000 |
| 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".