Alcohol use among Bhutanese refugees in Nepal
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore factors associated with alcohol use disorders (AUDs) among Bhutanese refugees in Nepal, where there has been a mass third-country resettlement operation in place since 2007. Design/methodology/approach – A case-control study was conducted in which the Alcohol Use Disorder Identification Test (AUDIT) was used to confirm AUDs and participants’ eligibility for placement in a case or control group. A translated questionnaire measuring demographic variables and context of drinking was also administered. Findings – In total, 128 questionnaires were completed (32 cases, 96 controls). Compared to participants without AUDs, those with AUDs were more likely to be older (36-50 years) (OR=10.5, 95 per cent CI 2.17-50.81), (50+years) (OR=10.3, 95 per cent CI 2.02-52.71), illiterate (OR=7.3 (2.80-18.42)), use tobacco (smoking or chewing) (OR=4.3 (1.84-10.01)) and be male (OR=3.5 (1.35-8.67)). Reasons for excessive alcohol use included unemployment, unoccupied time and increased family tensions. Originality/value – The study is the first of which the authors are aware that attempts to examine risk factors associated with AUDs within the context of a mass resettlement operation where camp services are winding down. The findings of this study suggest that greater attention needs to be given toward creating meaningful activities for adult, less educated male migrants awaiting resettlement.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".