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 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".