Barriers to Alcohol Screening Among Hypertensive Patients and the Role of Stigma: Lessons for the Implementation of Screening and Brief Interventions in European Primary Care Settings
Bibliographic record
Abstract
AIMS: 1. To quantify barriers to alcohol screening among hypertensive patients reported by primary healthcare professionals. 2. To examine whether education and screening frequency measures are associated with stigma-related barriers. METHODS: A web survey was conducted among 3081 primary healthcare professionals from France, Germany, Italy, Spain and the UK. Participants were asked about perceived barriers to alcohol screening as free-text response. The replies were independently categorized by two raters. Stigma-related barriers were predicted by logistic regressions with education, knowledge on alcohol as risk factor and frequency of alcohol screening. RESULTS: In France and Italy, almost half of the reported barriers were stigma-related, whereas time constraints were cited most commonly in Spain and the UK. In Germany, nearly half of respondents rated the importance of alcohol screening for hypertension as low. Perception that regular screening is inappropriate or associated with too much effort, beliefs that screening is unnecessary, and insufficient knowledge of screening tools were cited as further barriers. Professional education on alcohol use was consistently rated to be poorer than the equivalent education on hypertension, and only a minority of respondents perceived alcohol as important risk factor for hypertension. Stigma-related barriers could not be significantly predicted by education, knowledge or screening frequency in most models. CONCLUSIONS: Overall, regular alcohol screening among hypertensive patients seems to be widely accepted, but further education (Germany) and structural support (Spain, UK) could contribute to increase screening rates. In France and Italy, screening uptake could be improved by addressing stigma. SHORT SUMMARY: Alcohol screening among hypertensive patients was largely accepted among general practitioners from five different European countries. Reported screening barriers varied between countries and included time constraints, stigma and underrated importance of alcohol. Results did not indicate a positive impact of education and screening frequency on perception of stigma as barrier to screening.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".