Reaching and treating Spanish-speaking smokers through the National Cancer Institute's cancer information service
Bibliographic record
Abstract
Although the prevalence of smoking is lower among Hispanics than among the general population, smoking still levies a heavy public health burden on this underserved group. The current study, Adiós al Fumar (Goodbye to Smoking), was designed to increase the reach of the Spanish-language smoking cessation counseling service provided by the National Cancer Institute's Cancer Information Service (CIS) and to evaluate the efficacy of a culturally sensitive, proactive, behavioral treatment program among Spanish-speaking smokers. Adiós was a 2-group randomized clinical trial evaluating a telephone-based smoking cessation intervention. Spanish-speaking smokers (N = 297) were randomized to receive either standard counseling or enhanced counseling (EC). Paid media was used to increase the reach of the Spanish-language smoking cessation services offered by the CIS. The Adiós sample was of very low socioeconomic status (SES), and more than 90% were immigrants. Calls to the CIS requesting smoking cessation help in Spanish increased from 0.39 calls to 17.8 calls per month. The unadjusted effect of EC only approached significance (OR = 2.4, P = .077), but became significant after controlling for demographic and tobacco-related variables (OR = 3.8, P = .048). Adiós al Fumar demonstrated that it is possible to reach, retain, and deliver an adequate dose of treatment to a very low SES population that has traditionally been viewed as difficult to reach and hard to follow. Moreover, the findings suggest that a proactive, telephone-counseling program, based on the Treating Tobacco Use and Dependence Clinical Practice Guideline and adapted to be culturally appropriate for Hispanics, is effective. Cancer 2007. (c) 2006 American Cancer Society.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".