Understanding the relationship between socioeconomic status, smoking cessation services provided by the health system and smoking cessation behavior in Brazil
Bibliographic record
Abstract
Increasing the effectiveness of smoking cessation policies requires greater consideration of the cultural and socioeconomic complexities of smoking. The purpose of this paper is to explore the association between socioeconomic status and "selected midpoints" linked to smoking cessation in Brazil. Data was collected from a representative sample of urban adult smokers as part of the ITC-Brazil Survey (2009, N = 1,215). After controlling for age and gender, there were no statistically significant differences quit attempts in the last six months between individuals with different socioeconomic status. However, smokers with high socioeconomic status visited a doctor 1.54 times more often than those with low socioeconomic status (p-value = 0.017), and were also 1.65 times more likely to receive advice to quit smoking (p-value = 0.025). Our results demonstrate that disparities in health and socioeconomic status are still a major challenge for policymakers to increase the population impact of tobacco control actions worldwide.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".