Assessing Mental Health Outcomes of Political Violence and Civil Unrest in Peru
Bibliographic record
Abstract
BACKGROUND: Sustained political violence (SPV) may have long-term effects. AIMS: To assess mental and residual effects of exposure to SPV. To validate a post-traumatic stress disorder (PTSD) assessment tool in Quechua-speaking Peru. METHOD: Survey of 373 individuals aged 15 and over using the General Health Questionnaire (GHQ-12), Hopkins Symptom Checklist (HSCL-25) and a Trauma Questionnaire (TQ), derived from the Harvard Trauma Questionnaire. Sociodemographics were recorded. Reliability was assessed. Data reduction used factor analysis and modelling multiple regressions. RESULTS: A quarter of the sample had symptoms compatible with PTSD. Questionnaire reliability ranged from 0.81 to 0.89. Factor analysis confirmed high construct validity for TQ and HSCL-25. Modelling showed a strong association of PTSD-related symptoms and expressions of distress with the degree of exposure to SPV, especially among returnees. CONCLUSIONS: Long-term consequences of exposure to SPV take the form of PTSD, anxiety and depressive disorders, and culturally formulated expressions of distress. Some implications for clinicians are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".