Reframing political violence and mental health outcomes: outlining a research and action agenda for Latin America and the Caribbean region
Bibliographic record
Abstract
In recent decades, the number of people exposed to traumatic events has significantly increased as various forms of violence, including war and political upheaval, engulf civilian populations worldwide. In spite of widespread armed conflict, guerrilla warfare and political violence in the Latin American and Caribbean region, insufficient attention had been paid in assessing the medium and long-term psychological impact and additional burden of disease, death, and disability caused by violence and wars amongst civilian populations. Following a review of the literature, a few central questions are raised: What is the short, medium and long-term health impact of extreme and sustained forms of violence in a given population? How political violence is linked to poor mental health outcomes at the individual and collective levels? Are trauma-related disorders, universal outcomes of extreme and sustained violence? These questions lead us to reframe the analysis of political violence and mental health outcomes, and reexamine the notions of trauma, after which a research and action agenda for the region is outlined. In the concluding sections, some basic principles that may prove useful when designing psychosocial interventions in post-conflict situations are reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".