Terrorism, civil war and related violence and substance use disorder morbidity and mortality: A global analysis
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study is to examine associations between deaths owing to terrorism, civil war, and one-sided violence from 1994-2000 and substance use disorder disability-adjusted life years (DALYs). METHODS: The relationship between terrorism, and related violence and substance use disorder morbidity and mortality among World Health Organization Member States in 2002, controlling for adult per capita alcohol consumption, illicit drug use, and economic variables at baseline in 1994. RESULTS: Deaths as a result of terrorism and related violence were related to substance use disorder DALYs: a 1.0% increase in deaths as a result of terrorism, war and one-sided violence was associated with an increase of between 0.10% and 0.12% in alcohol and drug use disorder DALYs. Associations were greater among males and 15-44 year-old. CONCLUSION: Terrorism, war and one-sided violence may influence morbidity and mortality attributable to substance use disorders in the longer-term suggests that more attention to be given to rapid assessment and treatment of substance use disorders in conflict-affected populations with due consideration of gender and age differences that may impact treatment outcomes in these settings. Priorities should be established to rebuild substance abuse treatment infrastructures and treat the many physical and mental comorbid disorders.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".