Relationship of Anger with Alcohol use Treatment Outcome: Follow-up Study
Bibliographic record
Abstract
BACKGROUND: Anger is seen as comorbid condition in psychiatric conditions. It has an impact on one's quality of life. It leads to variation in the treatment outcome. The present study is going to explore the relationship of anger with treatment outcome among alcohol users after 1 year of treatment. The data for the present study were taken from the project work on correlates of anger among alcohol users, funded by center for addiction medicine, NIMHANS, Bengaluru, Karnataka, India. MATERIALS AND METHODS: A total of 100 males (50 alcohol-dependent and 50 abstainers) in the age range of 20-45 years with a primary diagnosis of alcohol dependence were taken for the study. They were administered a semi-structured interview schedule to obtain information about sociodemographic details, information about alcohol use, its relationship with anger and its effects on anger control and the State-Trait Anger Expression Inventory. RESULTS: 68% of the dependent and abstainers perceived anger as negative emotion and 76% in control perceived it as negative. The presence of significant difference was seen for relapsers group in relation to trait anger and state anger. The group who remained abstinent from the intake to follow-up differs significantly from the dependent group in relation to state anger and anger control out. Mean score was higher on trait anger for the dependent group. CONCLUSIONS: It has implication for anger management intervention/matching of treatment with users attributes and helping the users to develop the behavioral repertoires to manage anger.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".