Patient Perspectives on Discussions of Electronic Cigarettes in Primary Care
Bibliographic record
Abstract
PURPOSE: Patient preferences regarding the role of the primary care provider (PCP) in discussing electronic cigarette (e-cigarette) use are unknown. METHODS: < .05 to assess associations between e-cigarette use and these measures. RESULTS: The prevalence of e-cigarette use was 10% for recent (≤30 days) use and 29% for nonrecent (>30 days) use. Prevalence was significantly higher among those who were younger, less educated, or smoked cigarettes, but did not vary by sex or self-reported health status. Roughly one quarter of participants believed they were knowledgeable about the health effects of e-cigarettes, secondhand smoke, and quitting cigarettes. Sources of e-cigarette information included television advertisements (56.6%), friends and family (49.9%), or e-cigarette shops (25.5%), but included physician offices much less frequently (6.0%). Although 30.2% disagreed that their PCP knew a lot about e-cigarettes, 62.0% were comfortable discussing e-cigarettes with their PCP. However, only 25% of all patients wanted their PCP to discuss e-cigarettes with them, but 62.0% of recent e-cigarette users wanted such a discussion. Most preferred a brief discussion or handout to a lengthy discussion. CONCLUSION: PCPs were infrequent sources of information for patients regarding e-cigarette use. PCPs need evidence-based strategies to help them address e-cigarettes in primary care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".