The Frequency and Content of Discussions About Alcohol Use in Primary Care and Application of the Chief Medical Officer’s Low-Risk Drinking Guidelines: A Cross-Sectional Survey of General Practitioners and Practice Nurses in the UK
Bibliographic record
Abstract
AIMS: To examine how often general practitioners (GPs) and practice nurses (PNs) working in primary care discuss alcohol with patients, what factors prompt discussions, how they approach patient discussions and whether the Chief Medical Officers' (CMO) revised low-risk drinking guidelines are appropriately advised. METHODS: Cross-sectional survey with GPs and PNs working in primary care in the UK, conducted January-March 2017 (n = 2020). A vignette exercise examined what factors would prompt a discussion about alcohol, whether they would discuss before or after a patient reported exceeded the revised CMO guidelines (14 units per week) and whether the CMO drinking guidelines were appropriately advised. For all patients, participants were asked how often they discussed alcohol and how they approached the discussion (e.g. used screening tool). RESULTS: The most common prompts to discuss alcohol in the vignette exercise were physical cues (44.7% of participants) or alcohol-related symptoms (23.8%). Most practitioners (70.1%) said they would wait until a patient was exceeding CMO guidelines before instigating discussion. Two-fifths (38.1%) appropriately advised the CMO guidelines in the vignette exercise, with PNs less likely to do so than GPs (odds ratio [OR] = 0.77, P = 0.03). Less than half (44.7%) reportedly asked about alcohol always/often with all patients, with PNs more likely to ask always/often than GPs (OR = 2.22, P < 0.001). Almost three-quarters said they would enquire by asking about units (70.3%), compared to using screening tools. CONCLUSION: Further research is required to identify mechanisms to increase the frequency of discussions about alcohol and appropriate recommendation of the CMO drinking guidelines to patients.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".