Who receives treatment for alcohol use disorders in the European Union? A cross-sectional representative study in primary and specialized health care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorders (AUDs) are highly prevalent in Europe, but only a minority of those affected receive treatment. It is therefore important to identify factors that predict treatment in order to reframe strategies aimed at improving treatment rates. METHODS: Representative cross-sectional study with patients aged 18-64 from primary health care (PC, six European countries, n=8476, data collection 01/13-01/14) and from specialized health care (SC, eight European countries, n=1762, data collection 01/13-03/14). For descriptive purposes, six groups were distinguished, based on type of DSM-IV AUD and treatment setting. Treatment status (yes/no) for any treatment (model 1), and for SC treatment (model 2) were main outcome measures in logistic regression models. RESULTS: AUDs were prevalent in PC (12-month prevalence: 11.8%, 95% confidence interval (CI): 11.2-12.5%), with 17.6% receiving current treatment (95%CI: 15.3-19.9%). There were clear differences between the six groups regarding key variables from all five predictor domains. Prediction of any treatment (model 1) or SC treatment (model 2) was successful with high overall accuracy (both models: 95%), sufficient sensitivity (model 1: 79%/model 2: 76%) and high specificity (both models: 98%). The most predictive single variables were daily drinking level, anxiety, severity of mental distress, and number of inpatient nights during the last 6 months. CONCLUSIONS: Variables from four domains were highly predictive in identifying treatment for AUD, with SC treatment groups showing very high levels of social disintegration, drinking, comorbidity and functional losses. Earlier intervention and formal treatment for AUD in PC should be implemented to reduce these high levels of adverse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it