The Hong Kong Survey on the Epidemiology of Trauma Exposure and Posttraumatic Stress Disorder
Bibliographic record
Abstract
This study examined the epidemiology of trauma exposure (TE) and posttraumatic stress disorder (PTSD) among community-dwelling Chinese adults in Hong Kong. Multistage stratification sampling design was used, and 5,377 participants were included. In Phase 1, TE, probable PTSD (p-PTSD), and psychiatric comorbid conditions were examined. In Phase 2, the Structured Clinical Interview for the DSM-IV (SCID-I) was used to determine the weighted diagnostic prevalence of lifetime full PTSD. Disability level and health service utilization were studied. The findings showed that the weighted prevalence of TE was 64.8%, and increased to 88.7% when indirect TE types were included, with transportation accidents (50.8%) reported as the most common TE. The prevalence of current p-PTSD among participants with TE was 2.9%. Results of logistic regression suggested that nine specific trauma types were significantly associated with p-PTSD; among this group, severe human suffering, sexual assault, unwanted or uncomfortable sexual experience, captivity, and sudden and violent death carried the greatest risks for developing PTSD, odds ratio (OR) = 2.32-2.69. The occurrence of p-PTSD was associated with more mental health burdens, including (a) sixfold higher rates for any past-week common mental disorder, OR = 28.4, (b) more mental health service utilization, p < .001, (c) poorer mental health indexes in level of symptomatology, suicide ideation and functioning, p < .001, and (d) more disability, ps < .001-p = .014. The associations found among TE, PTSD, and health service utilization suggest that both TE and PTSD should be considered public health concerns.
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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 | 0.000 | 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".