Attitudes toward Precision Treatment of Smoking in the Southern Community Cohort Study
Bibliographic record
Abstract
Abstract Background: Precision interventions using biological data may enhance smoking treatment, yet are understudied among smokers who are disproportionately burdened by smoking-related disease. Methods: We surveyed smokers in the NCI-sponsored Southern Community Cohort Study, consisting primarily of African-American, low-income adults. Seven items assessed attitudes toward aspects of precision smoking treatment, from undergoing tests to acting on results. Items were dichotomized as favorable (5 = strongly agree/4 = agree) versus less favorable (1 = strongly disagree/2 = disagree/3 = neutral); a summary score reflecting generalized attitudes was also computed. Multivariable logistic regression tested independent associations of motivation (precontemplation, contemplation, and preparation) and confidence in quitting (low, medium, and high) with generalized attitudes, controlling for sociodemographic factors and nicotine dependence. Results: More than 70% of respondents endorsed favorable generalized attitudes toward precision medicine, with individual item favorability ranging from 64% to 83%. Smokers holding favorable generalized attitudes reported higher income and education (P < 0.05). Predicted probabilities of favorable generalized attitudes ranged from 63% to 75% across motivation levels [contemplation vs. precontemplation: adjusted odds ratio (AOR) = 2.10, 95% confidence interval (CI), 1.36–3.25, P = 0.001; preparation vs. precontemplation: AOR = 1.83, 95% CI, 1.20–2.78, P = 0.005; contemplation vs. preparation: AOR = 1.15, 95% CI, 0.75–1.77, P = 0.52] and from 59% to 78% across confidence (medium vs. low: AOR = 1.91, 95% CI, 1.19–3.07, P = 0.007; high vs. low: AOR = 2.62, 95% CI, 1.68–4.10, P < 0.001; medium vs. high: AOR = 0.73, 95% CI, 0.48–1.11, P = 0.14). Conclusions: Among disproportionately burdened community smokers, most hold favorable attitudes toward precision smoking treatment. Individuals with lower motivation and confidence to quit may benefit from additional intervention to engage with precision smoking treatment. Impact: Predominantly favorable attitudes toward precision smoking treatment suggest promise for future research testing their effectiveness and implementation.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".