Drinking Parameters and Associated Factors among Alcoholics Attending De-Addiction Center at a Tertiary Care Hospital, Jaipur
Bibliographic record
Abstract
Introduction: Alcohol addiction is influenced by factors like started drinking at early age, positive family history, impulsivity, hyperactivity. Objective of the study was to assess drinking parameters and associated factors among alcohol addicts attending De-addiction center. Methods: This observational study was conducted during 1st June 2015 to 31st May 2016. Total 400 cases with an AUDIT score of >8 were included. Information like duration of drinking, amount of drink, type of alcohol consumed, motivating factors for seeking de addiction centre, previous treatment history were collected. Results: Half of alcoholics started drinking at very young age (<26 years). Majority were Hindu and one fourth cases had primary education and one fourth was literate up to higher secondary. Half of cases were daily wages and majority were from SES class III & IV. Only 16% had positive family history. Majority (80%) consumed more than 3 to 6 quarter of alcohol daily. Majority (79%) consumed “Desi” alcohol. Conclusion: When alcohol is started late then the amount of alcohol consumed is less. Per capita income has a negative 18.2% change in amount of alcohol. Age of starting consumption of alcohol also made a significant contribution to predict type of alcohol consumed.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".