Secondhand Tobacco Smoke Exposure, Nicotine Dependence, and Smoking Cessation
Bibliographic record
Abstract
OBJECTIVE: To explore the association among the number of sources of secondhand tobacco smoke (SHS) exposure, nicotine dependence (ND), and smoking cessation. DESIGN: A secondary analysis of cross-sectional data. Responses for the main study were obtained in 2001 from a controlled trial of the Quit and Win Tobacco Free Contest in Kentucky. SAMPLE: 822 current smokers. MEASUREMENTS: Demographic variables (age, gender, educational status, income, and ethnicity) the number of sources of SHS exposure, smoking frequency, length of abstinence from smoking, age of smoking initiation, smoking cessation attempts, intentions to quit smoking, and ND. RESULTS: The number of sources of SHS exposure was associated with higher ND and smoking frequency, and related to low intentions and attempts to quit smoking. The number of sources of SHS exposure contributed to 11% of the variance in the final ND model, after accounting for control and potential mediating variables. CONCLUSIONS: The number of sources of SHS exposure may be an important factor influencing ND and intentions and attempts to quit smoking. Further studies are needed to explore the association between SHS exposure and ND among smokers to guide treatment and policy development.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".