Alcohol Use Disorders in Primary Health Care: What Do We Know and Where Do We Go?
Bibliographic record
Abstract
AIMS: To analyze the current paradigm and clinical practice for dealing with alcohol use disorders (AUD) in primary health care. METHODS: Analyses of guidelines and recommendations, reviews and meta-analyses. RESULTS: Many recommendations or guidelines for interventions for people with alcohol use problems in primary health care, from hazardous drinking to AUD, can be summarized in the SBIRT principle: screening for alcohol use and alcohol-related problems, brief interventions for hazardous and in some cases harmful drinking, referral to specialized treatment for people with AUD. However, while there is some evidence that these procedures are effective in reducing drinking levels, they are rarely applied in clinical practice in primary health care, and no interventions are initiated, even if the primary care physician had detected problems or AUD. Rather than asking primary health care physicians to conduct interventions which are not typical for medical doctors, we recommend treatment initiation for AUD at the primary health care level. AUD should be treated like hypertension, i.e. with regular checks for alcohol consumption, advice for behavioral interventions in case of consumption exceeding thresholds, and pharmaceutical assistance in case the behavioral interventions were not successful. Minimally, alcohol consumption should be screened for in all situations where there is a co-morbidity with alcohol being a potential cause (such as hypertension, insomnia, depression or anxiety disorders). CONCLUSIONS: A paradigm shift is proposed for dealing with problematic alcohol consumption in primary health care, where initiation for treatment for AUD is seen as the central element.
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.
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 | 0.035 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".