Previous Violent Events and Mental Health Outcomes in Guatemala
Bibliographic record
Abstract
OBJECTIVES: We analyzed a probability sample of Guatemalans to determine if a relationship exists between previous violent events and development of mental health outcomes in various sociodemographic groups, as well as during and after the Guatemalan Civil War. METHODS: We used regression modeling, an interaction test, and complex survey design adjustments to estimate prevalences and test potential relationships between previous violent events and mental health. RESULTS: Many (20.6%) participants experienced at least 1 previous serious violent event. Witnessing someone severely injured or killed was the most common event. Depression was experienced by 4.2% of participants, with 6.5% experiencing anxiety, 6.4% an alcohol-related disorder, and 1.9% posttraumatic stress disorder (PTSD). Persons who experienced violence during the war had 4.3 times the adjusted odds of alcohol-related disorders (P < .05) and 4.0 times the adjusted odds of PTSD (P < .05) compared with the postwar period. Women, indigenous Maya, and urban dwellers had greater odds of experiencing postviolence mental health outcomes. CONCLUSIONS: Violence that began during the civil war and continues today has had a significant effect on the mental health of Guatemalans. However, mental health outcomes resulting from violent events decreased in the postwar period, suggesting a nation in recovery.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".