Association between Socio-Demographics and Alcohol Dependence among Individuals Living in an Indian Setting
Bibliographic record
Abstract
BACKGROUND: Alcohol use is on the rise worldwide and urgent steps are required to curb this growing burden of alcohol consumption. Alcohol drinking leads to serious social, physical and mental consequences. OBJECTIVE: The objective of this pilot study is to examine association between socio-demographics and severity of alcohol dependence among individuals obtaining treatment at alcohol de-addiction center. METHODS: This pilot cross sectional study was conducted in September 2013 in South India. A convenient sample of 100 participants was enrolled. Individuals aged 30 years and above, receiving treatment from de-addiction center and providing written informed consent were eligible for the study. A modified version of previously validated questionnaires was used for gathering information on socio-demographic characteristics, severity of alcohol dependence (using Alcohol Dependent Scale [ADS] and Short Alcohol Dependence Data questionnaire [SADD]), motivational incentives for alcohol quitting and challenges faced while quitting alcohol. RESULTS: All participants were males with mean age of 43 years (SD = 6.5 years). Significant association was seen between ADS and annual income (p = 0.001), education (p = 0.001), occupation (p < 0.0001) and work timing (p < 0.0001). Similar results were seen with SADD scores. Family support (100%) and health (60%) were reported to be the most important motivating factors for quitting alcohol. DISCUSSION: Results showed an urgent need of interventions that are family centered and focus on unskilled, less educated individuals having high work stress. Public health interventions should not only be home based, but should also include worksite awareness initiatives. A national policy is needed to promote alcohol quitting and to bring awareness regarding the consequences of alcohol consumption on individual's life.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".