Attitudes and Beliefs towards Patients with Hazardous Alcohol Use: A Systematic Review
Bibliographic record
Abstract
Objective. To describe emergency department (ED) staff attitudes and beliefs towards patients presenting with hazardous alcohol use and their clinical management. Methods. A search of MEDLINE, EMBASE, CINAHL, SCOPUS from 1990 to 2010, and reference lists from included studies was conducted. Two reviewers independently screened for inclusion and assessed study quality. One reviewer extracted the data and a second checked for completeness and accuracy. Results. Among nine studies four reported varied beliefs on whether screening was worthwhile for identifying hazardous alcohol use (physicians: 42%–88%; nurses: 50%–100%). Physicians in three studies were divided on intervention provision (32%–54% in support of intervention provision) as were nurses in two studies (39% and 64% nurses in support of intervention provision). Referral for treatment was identified in two studies as an important part of ED management (physicians: 62% and 97%; nurses: 95%). Other attitudes and beliefs identified across the studies included concern that asking about alcohol consumption would be seen as obtrusive or offensive, and a perceived lack of time and resources available for providing care and referrals. Conclusions. ED staff had varying attitudes towards ED management of patients with hazardous alcohol use. Investigations into improving clinical care for hazardous alcohol use are needed to optimize ED management for these 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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".