A Systematic Review on Harmful Alcohol Use Among Civilian Populations Affected by Armed Conflict in Low- and Middle-Income Countries
Bibliographic record
Abstract
Background: There are currently over 55 million refugees and internally displaced persons due to armed conflict. In addition, there are around 150 million more conflict-affected residents who remain in their home communities. Armed conflict poses a number of potential risks for harmful alcohol use. Objective: The objective of the study was to systematically examine evidence on harmful alcohol use among conflict-affected populations in low- and middle-income countries. Methods: A systematic review methodology was used following PRISMA guidelines. Quantitative studies were selected with outcomes relating to harmful alcohol use among conflict-affected populations in low- and middle-income countries. Seven bibliographic databases and a range of gray literature sources were searched. Descriptive analysis was applied and a quality assessment conducted using the Newcastle-Ottawa Quality Assessment Scale. Results: The search yielded 10,037 references of which 22 studies met inclusion criteria. Twenty-one of the studies used a cross-sectional design, and 1 used a case series design. Evidence on risk factors for harmful alcohol use was weak overall. Factors associated with harmful alcohol use were male gender, older age, cumulative trauma event exposure, and depression. There were no studies on the effectiveness of interventions for harmful alcohol use. The strength of evidence was also limited by the generally moderate quality of the studies. Conclusions: Substantially more evidence is required to understand the scale of conflict-associated harmful alcohol use, key risk factors, association of alcohol use with physical and mental disorders, and effectiveness of interventions to address harmful alcohol use in conflict-affected populations.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".