War, forced displacement, and alcohol abuse: experiences and perceptions of war-affected south Sudanese refugee youth living in Bidibidi refugee settlement in northern Uganda
Bibliographic record
Abstract
Refugees are at high risk of alcohol abuse due to their experiences of structural, physical, sexual, and psychological violence in their countries of origin, during flight, and within host communities. Given the prolonged civil war in their country, South Sudanese have continued to flee profound forms of violence and now constitute the largest population of refugees in Uganda. However, little is known about their displacement experiences, as well as the reality of alcohol use and abuse within refugee settlements. Drawing upon the direct voices of a sample of war-affected South Sudanese young people, this article explores their experiences of forced displacement and their links to alcohol abuse, as well as their perceptions regarding appropriate alcohol treatment interventions for refugees in the camp. A total of 22 semi-structured qualitative interviews were conducted with 14 refugee youth (aged 18-25) alongside eight adult key informants who work with the youth (religious leaders, sports coaches, educators, social workers, and settlement administrators). Using thematic analysis, the study revealed a series of key themes influencing and shaping the high incidence of alcohol abuse among the youth. These included traumatic wartime and migration experiences, family separation, poor prospects, and the ubiquitous availability of alcohol in the settlement. In addition, we show how alcohol operates as a strategic tool for survival for the youth, as well as highlight how these perceptions can help to inform alcohol treatment interventions in the Bidibidi refugee settlement. To our knowledge, this is the first in-depth study of alcohol abuse among war-affected South Sudanese refugee youth in Uganda, addressing a significant gap in the current literature on war-affected youth, forced displacement, and alcohol abuse. We contend that involving youth in the design of interventions can be helpful for culturally sensitive and relevant prevention, treatment, and care in refugee settings. In addition, providing employment opportunities and meaningful engagement for growth through social participation can help to address harmful alcohol use among youth in the camps.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".