Mental Health Problems among Children One-Year after Sichuan Earthquake in China: A Follow-up Study
Bibliographic record
Abstract
BACKGROUND: On May 12, 2008, a destructive earthquake registering 8.0 on the Richter scale struck Sichuan Province, southwest China. Beichuan County was the epicenter which was one of the areas nearly completely destroyed by the earthquake. In Beichuan, about 15000 people died and 3000 people were missing. Specially, the earthquake took 1587 students' and 214 teachers' lives from the elementary and middle schools there. The main purpose of the study was to provide a better understanding of mental health problems and associated risk factors among children after earthquake. METHOD: Three hundred and thirty grades 3-5 children completed the questionnaire of disaster -related experience and the Trauma Symptom Checklist for Children-Alternate Version (TSCC-A). The first survey was carried out six months after the earthquake, and the second one was carried out six months later. The measurements and methodology applied in the two sessions were identical. RESULTS: The prevalence rates of the problems at two time-points were 23.3% and 22.7% for anxiety, 14.5% and 16.1% for depression, and 11.2% and 13.4% for PTSD, respectively. Among demographic variables, no significant age difference existed, while it was found that 6 months after the earthquake, symptoms of anxiety, depression and PTSD were significantly more common among students in grades 4 and 5 than those in grade 3, Initial exposure to death, bereavement and extreme fear were significant predictive factors for the occurrence of anxiety, depression and PTSD. CONCLUSIONS: Findings of this study suggest that posttraumatic mental health problems after natural disaster in children may have reached epidemic proportions and remain high for a long period. Psychologist and social workers should pay more attention to children who experienced more traumatic stresses and provide appropriate mental health interventions. Implications and limitations of these findings were discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".