Proceedings of the 17th Annual Conference on the Science of Dissemination and Implementation in Health
Bibliographic record
Abstract
BackgroundUnhealthy alcohol use is a leading cause of preventable deaths and is associated with many social and health problems.Less than a third of people who visit primary care providers in the US are asked about or ever discuss alcohol use with a health professional.The first aim of the Stop Unhealthy Alcohol Use Now (STUN) trial was to evaluate the effect of primary care practice facilitation on uptake of evidencebased screening and brief counseling for unhealthy alcohol use.Methods STUN was a hybrid implementation effectiveness trial that enrolled primary care practices across the state of North Carolina.Enrolled practices received twelve months of practice facilitation, including quality improvement coaching, electronic health record (EHR) support (e.g., using available tools, creating smart phrases or flowsheets, retrieving data), and training on screening and counseling for unhealthy alcohol use.The primary outcome measures included the change from three months prior to baseline through the second quarter (i.e., months 4-6) in the number and percentage of adult patients (1) who were screened for unhealthy alcohol use and (2) who received brief counseling after a positive screening result.For count data, we used negative binomial mixed-effects models to assess trajectories; models accounted for clinic size.For percentage data, we utilized linear mixed models.Findings Twenty one practices serving 54,294 patients reported implementation effectiveness data.Screening rates increased significantly, from an average of 200 to over 400 adults per quarter per practice (from 20% to 50% of adult patients, p < 0.01).Additionally, the number and percentage of patients who received a brief intervention after a positive screening result increased from 0 to 12 adults per quarter per practice (from 0% to 40% of adults with a positive screen, p<0.01).After month 6, assessment of the implementation effectiveness outcomes showed reasonable sustainment.There was significant variability across participating practices for screening and counseling outcomes.Implications for D&I Research: Our findings provide evidence for the positive effect of practice facilitation on uptake of evidence-based screening and counseling for unhealthy alcohol use when delivered to small to medium-sized primary care practices.
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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 | 0.127 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.134 | 0.042 |
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".