Psychological and Pharmacological Interventions to Reduce Alcohol Use Disorder (AUD) in the inpatient units. A General Review.
Bibliographic record
Abstract
Introduction According to the World Health Organization, around 2 billion people worldwide are estimated to drink. Alcohol intake results in 25% of the 3.8% of worldwide fatalities and 4.6% of global disability-adjusted life years that may be attributed to alcohol Objectives This review seeks to synthesize data on psychological and pharmacological treatments for Alcohol Use Disorder (AUD) available in the inpatient setting. Methods A comprehensive and narrative review of studies and research on psychological and pharmacological interventions for patients with alcohol use disorders in inpatient treatment units was performed. Data was extracted from electronic bibliographic databases, including Medline, EMBASE, PsycINFO, Global Health, HealthSTAR, and Cumulative Index for Nursing and Allied Health Literature (CINAHL) via EBSCOhost. This review included both qualitative and quantitative studies Results Overall, after an initial title, abstract screening, and subsequent full-text screening, seven out of 1245 extracted studies met the eligibility criteria and were included in the review. This review suggests that a combination of pharmacological interventions such as naltrexone, nalmefene, acamprosate and brief psychological interventions were effective in treating AUD. Conclusions This review suggests that pharmacological and psychological approaches, when used together, are efficacious in treating AUD. There is a need to adopt both pharmacological and psychological interventions in the treatment of AUD. Disclosure of Interest None Declared
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".