Use of Provider-Based Complementary and Alternative Medicine by Adult Smokers in the United States: Comparison from the 2002 and 2007 NHIS Survey
Bibliographic record
Abstract
PURPOSE: To provide a snapshot of provider-based complementary and alternative medicine (pbCAM) use among adult smokers and assess the opportunity for these providers to deliver tobacco cessation interventions. DESIGN: Cross-sectional analysis of data from the 2002 and 2007 National Health Interview Surveys. SETTING: Nationally representative sample. SUBJECTS: A total of 54,437 (31,044 from 2002; 23,393 from 2007) adults 18 years and older. MEASURES: The analysis focuses on 10 types of pbCAM, including acupuncture, Ayurveda, biofeedback, chelation therapy, chiropractic care, energy therapy, folk medicine, hypnosis, massage, and naturopathy. ANALYSIS: The proportions of current smokers using any pbCAM as well as specific types of pbCAM in 2002 and 2007 are compared using SAS SURVEYLOGISTIC. RESULTS: Between 2002 and 2007, the percentage of recent users of any pbCAM therapy increased from 12.5% to 15.4% (p = .001). The largest increases occurred in massage, chiropractic, and acupuncture. Despite a decrease in the national average of current smokers (22.0% to 19.4%; p = .001), proportions of smokers within specific pbCAM disciplines remained consistent. CONCLUSION: Complementary and alternative medicine (CAM) practitioners, particularly those in chiropractic, acupuncture, and massage, represent new cohorts in the health care community to promote tobacco cessation. There is an opportunity to provide brief tobacco intervention training to CAM practitioners and engage them in public health efforts to reduce the burden of tobacco use in the United States.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".