‘He was trapped in his own web’—Dependent drinking as a poverty trap: A qualitative study from Goa, India
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Of the Indian population, 2.7% have alcohol dependence, the most severe of alcohol use disorders. Alcohol use disorders have previously been found to be correlated with a range of negative economic outcomes, but dependent drinking has yet to be causally identified as a poverty trap. We use qualitative data as the first step towards identifying the mechanisms that may underlie a dependent drinking driven poverty trap in India. DESIGN AND METHODS: Thirty-six in-depth interviews were conducted and analysed using inductive thematic analysis. Participants were men having probable alcohol dependence (n = 11), doctors (n = 13) who come into contact with patients presenting with alcohol dependence at government hospitals and clinics, and family members of men with probable alcohol dependence (n = 12) in Goa, India. RESULTS: Our key findings showed that families of those who have alcohol dependence have less opportunity for saving, more job instability and poor treatment opportunity to aid recovery and allow escaping from the trap. DISCUSSION AND CONCLUSIONS: Households in Goa, India with a member with alcohol dependence display patterns consistent with a poverty trap, though the mechanisms derived from these qualitative data need to be further demonstrated by longitudinal quantitative data to corroborate a causal relationship between alcohol use disorders and poverty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".