Screening auf problematischen Alkoholkonsum – Erhebung zur Umsetzung der S3-Leitlinienempfehlungen in der transdisziplinären Versorgung einer Modellregion
Bibliographic record
Abstract
AIM: Recording the frequency of screenings for problematic alcohol consumption by professionals involved in the health care of respective patients. The German S3-guideline "screening, diagnosis and treatment of alcohol-related disorders" recommends the use of questionnaire-based screenings for all patients in all settings. METHODS: Cross-sectional survey on screening frequency among general practitioners, gynecologists, psychiatrists, child- and adolescent therapists, psychotherapists, social workers and midwives. Logistic regression was used to explore how healthcare professionals' attributes were associated with the implementation of screenings. RESULTS: With response rates of about 20%, health care professionals reported using screening instruments for an average of 6.9% of all patients during the previous four weeks. Most of the time, custom-made questions were used instead of the recommended instruments (AUDIT, AUDIT-C). Higher screening rates were reported for patients with newly diagnosed hypertension (21.2%), alcohol-related disorders (43.3%) and mental disorders (39.3%). Knowledge of the guideline was associated with implementation of screenings (OR=4.67; 95% KI 1.94-11.25, p<0.001). CONCLUSIONS: Comprehensive screening for problematic alcohol use with questionnaire-based instruments in accordance with guidelines is far from being routinely implemented in the studied health care settings. Measures to increase the knowledge of the guidelines are necessary in order to increase the frequency of alcohol screening in 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".