Alcohol dependence treatment in the EU: A literature search and expert consultation about the availability and use of guidelines in all EU countries plus Iceland, Norway, and Switzerland
Bibliographic record
Abstract
Rehm, J., Rehm, M. X., Alho, H., Allamani, A., Aubin, H., Bühringerm G,m Daeppen, J., Frick, U., Gual, A., & Heather, N. (2013). Alcohol dependence treatment in the EU: A literature search and expert consultation about the availability and use of guidelines in all EU countries plus Iceland, Norway, and Switzerland. International Journal of Alcohol and Drug Research, 2(2), 53-67. doi: 10.7895/ijadr.v2i2.89 (http://dx.doi.org/10.7895/ijadr.v2i2.89)Aim: To describe guidelines and common practices for alcohol dependence treatment in Europe.Design: Systematic and qualitative review; for each country, guidelines were identified via systematic literature research, followed by interviews with treatment experts.Setting: European Union (EU) countries plus Iceland, Norway, and Switzerland.Participants: Experts in alcohol dependence treatments and treatment systems.Measure: Semi-structured questionnaire for interviews.Findings: While fewer than half of EU countries have formal national guidelines for alcohol dependence treatment, a majority of these countries have guidelines by professional organizations such as psychiatric or neuropsychopharmacologic societies, and several are currently developing such guidelines. Abstinence is the usual treatment goal, but the majority of countries accept reduction of drinking as an intermediate or secondary goal, in practice even more than in the guidelines. Psychotherapy, mainly cognitive-behavioral approaches, motivational interviewing, and family therapy, is the most common treatment for relapse prevention, in part accompanied by pharmacotherapy (disulfiram, acamprosate and naltrexone being used most often).Conclusions: There are differences in treatment for alcohol dependence in Europe. The introduction of reduction of drinking as one treatment goal may attract more patients.
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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 | 0.036 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".