Inpatient Tobacco Cessation Counseling and Treatment
Bibliographic record
Abstract
BACKGROUND: Hospitalization is an ideal time to provide tobacco cessation counseling, but little is known about current practices of inpatient tobacco treatment in the United States. OBJECTIVES: The objective of the study was to describe the prevailing practices and perceptions regarding inpatient tobacco cessation treatment and counseling among U.S. Cardiologists. METHODS: A 27-question survey was developed, pretested, and administered to 498 cardiologists through the American College of Cardiology CardioSurve program. RESULTS: In total, 122 (24%) participants responded, of which 100 were eligible and completed the survey, achieving our expected response rate. The sample was generally representative of U.S. cardiologists. Cardiologists reported routinely screening for cigarette use (92%) but only a minority (20%) reported routinely referring patients for additional tobacco cessation support. Most cardiologists (74%) reported using the nicotine patch on occasion, but few ever used varenicline (31%) or bupropion (28%). A minority (12%) reported working at a hospital with an inpatient smoking cessation team, but many (41%) expressed interest in championing such a program. Administrative, financial, and time constraints were commonly reported barriers to successful deployment of an inpatient smoking cessation program. CONCLUSIONS: Although tobacco cessation is critical posthospitalization, most hospitals do not have tobacco cessation teams, and most cardiologists do not assist their patients in quit attempts. Although improving knowledge among cardiologists is important, policy changes to incentivize hospital-wide adoption of such programs is crucial to increase availability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".