Contextual predictors of AUDIT scores among adult men living in India
Bibliographic record
Abstract
Madden, D., Rathi, L., Stewart, A., & Clapp, J. (2017). Contextual predictors of AUDIT scores among adult men living in India. The International Journal Of Alcohol And Drug Research, 6(1), 53-58. doi:http://dx.doi.org/10.7895/ijadr.v6i1.241Introduction: Currently, little is known about the prevalence of alcohol use in India. In order to begin to address this knowledge gap, this exploratory study examined contextual aspects of drinking events and the relationship between these factors and high-risk drinking.Methods: A convenience sample of 198 adult men was recruited from rural areas adjacent to the city of Nagpur. Participants were sampled in two waves. Respondents in both waves completed a nine-item survey that addressed alcohol use, including motivation to drink, where one drinks, and with whom one drinks. Demographic characteristics (e.g., income) were also recorded. Respondents recruited in the second wave (n = 98) completed the Alcohol Use Disorders Identification Test (AUDIT). The data were analyzed using Poisson regression models.Results: Of those who completed the AUDIT, 37% were at high risk for developing an alcohol-use disorder (i.e., received a score of 20 or greater). Participants had higher AUDIT scores (i.e., alcohol-use problems) when they reported typically buying alcohol in a shop. Furthermore, respondents with greater weekly incomes and those who drink with the motivation to get very drunk have higher AUDIT scores.Conclusions: This study found an alarmingly high rate of alcohol use and alcohol-related issues among respondents. A better understanding of drinking patterns and contextual aspects of drinking events is warranted.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".