Non-smokers seeking help for smokers: a preliminary study
Bibliographic record
Abstract
OBJECTIVES: To examine the phenomenon of non-smokers spontaneously taking action to seek help for smokers; to provide profiles of non-smoking helpers by language and ethnic groups. SETTING: A large, statewide tobacco quitline (California Smokers' Helpline) in operation since 1992 in California, providing free cessation services in English, Spanish, Mandarin, Cantonese, Korean, and Vietnamese. SUBJECTS: Callers between August 1992 and September 2005 who identified themselves as either white, black, Hispanic, American Indian, or Asian (n = 349,110). A subset of these were "proxies": callers seeking help for someone else. For more detailed analysis, n = 2143 non-smoking proxies calling from October 2004 through September 2005. MAIN OUTCOME MEASURES: Proportions of proxies among all callers in each of seven language/ethnic groups; demographics of proxies; and proxies' relationships to smokers on whose behalf they called. RESULTS: Over 22 000 non-smoking proxies called. Proportions differed dramatically across language/ethnic groups, from mean (+/-95% confidence interval) 2.7 (0.3)% among English-speaking American Indians through 9.3 (0.3)% among English-speaking Hispanics to 35.3 (0.7)% among Asian-speaking Asians. Beyond the differences in proportion, however, remarkable similarities emerged across all groups. Proxies were primarily women (79.2 (1.7)%), living in the same household as the smokers (65.0 (2.1)%), and having either explicit or implicit understandings with the smokers that calling on their behalf was acceptable (90.0 (1.3)%). CONCLUSIONS: The willingness of non-smokers to seek help for smokers holds promise for tobacco cessation and may help address ethnic and language disparities. Non-smoking women in smokers' households may be the first group to target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".