Standardizing Tobacco Cessation Counseling Using the 5 A's Intervention
Bibliographic record
Abstract
BACKGROUND: Tobacco use is the leading cause of preventable death due to cardiovascular disease. LOCAL PROBLEM: Tobacco cessation counseling (TCC) is varied among providers, leading to suboptimal willingness to make a quit attempt. METHODS: We used a quality improvement framework to pilot the 5 A's for TCC from April 2021 to August 2021 in our outpatient cardiology clinic. INTERVENTIONS: Providers implemented TCC using the 5 A's intervention. Patient follow-up phone calls were conducted 30 days after receiving TCC. RESULTS: Of 629 patient encounters, the mean TCC rate increased by 27.5%, and the mean reported cessation rates improved by 3.9%. Variation among providers decreased for TCC rates when they used the 5 A's intervention. CONCLUSIONS: The 5 A's intervention standardized TCC efforts. Using the 5 A's led to an increase in patients who reported smoking cessation 30 days after TCC was received.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".