The role of alcohol in the management of hypertension in patients in European primary health care practices – a survey in the largest European Union countries
Bibliographic record
Abstract
BACKGROUND: Even though addressing lifestyle problems is a major recommendation in most guidelines for the treatment of hypertension (HTN), alcohol problems are not routinely addressed in the management of hypertension in primary health care. METHODS: Internet based survey of 3081 primary care physicians, recruited via the mailing lists of associations for general practitioners (GPs) in France, Germany, Italy, Spain and the UK. Clinical practice, attitudes, knowledge, education and training were assessed. Logistic regression to predict screening, brief intervention and treatment for alcohol dependence in the management of hypertension were assessed. RESULTS: Overall, about one third of the interviewed GPs reported sufficient screening in cases with HTN (34.0 %, 95 % confidence interval (CI):32.1-35.8 %). One out of five GPs screened and delivered brief interventions in HTN patients with hazardous consumption (22.2 %, 95 % CI: 20.6-23.8 %) and about one in 13 GPs provided treatment for HTN patients with alcohol dependence other than advice or brief intervention (7.8 %, 95 % CI: 6.8-8.9 %). Post-graduate training and belief in their effectiveness predicted interventions. There were marked differences between countries. CONCLUSIONS: While current interventions were overall low, marked differences between countries indicate that current practices could be improved. Education and post-graduate training seems to be key in improving clinical practice of including interventions for problematic alcohol consumption and alcohol dependence in primary health care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".