An Epidemiologic Study of Posttraumatic Stress Disorder in Flood Victims in Hunan China
Bibliographic record
Abstract
OBJECTIVE: To estimate the occurrence and to assess the determinants of posttraumatic stress disorder (PTSD) in flood victims. METHOD: We carried out a retrospective study to examine the occurrence and the determinants of PTSD in victims of flood in 1998 and 1999 in Hunan, China. We used multistage sampling to select the subjects from the flood areas, and we ascertained PTSD according to DSM-IV criteria. Data were collected in face-to-face interviews carried out by experienced research assistants using a preconstructed questionnaire. We used a multiple logistic regression model to analyze the data. RESULTS: A total of 33 340 subjects (86.0% of the selected subjects, aged 7 years or over) in the study villages were interviewed. Among them, 2875 (8.6%) had symptoms that met the diagnostic criteria for PTSD. Significant risk factors for PTSD included female sex (odds ratio [OR] 1.12; 95% confidence interval [CI], 1.04 to 1.21), older age (age 18 to 59 years OR 2.28; 95%CI, 2.02 to 2.57, and age > or = 60 years OR 2.42; 95%CI, 2.05 to 2.85), flood type (collapsed embankment OR 1.84; 95%CI, 1.64 to 2.05, and flash flood OR 3.12; 95%CI, 2.76 to 3.52), and flood severity (intermediate OR 4.05; 95%CI, 3.55 to 4.62, and severe OR 2.98; 95%CI, 2.60 to 3.41). CONCLUSIONS: PTSD is a common mental disorder in flood victims, which implies the need for improved health services, especially mental health services, for this 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".