Association Between Socioeconomic Status and Access to Care and Quitting Smoking With and Without Assistance
Bibliographic record
Abstract
INTRODUCTION: Socio-economic disparities in smoking rates persist, in Ontario, despite public health care and universal tobacco control policies. Mechanisms for continuing disparities are not fully understood. Unequal access or utilization of assistance for cessation may contribute. The objective of this research was to use longitudinal data on smokers to examine the associations between socioeconomic status (SES) and access to care measures and assisted and unassisted quit attempts. METHODS: Data were taken from 3578 smokers with at least one follow-up interview participating in the Ontario Tobacco Survey (OTS). Multinomial regression models with imputed missing values were run for each measure of SES and access to care to assess the association with quitting behavior and use of assistance, unadjusted and while adjusting for smoking history and demographic covariates. RESULTS: Adjusted analyses found smokers living in areas with the lowest ethnic concentration were more likely to make an assisted quit attempt compared to unassisted quitting (RR = 1.64; 95% CI = 1.08-2.50) or making no quit attempt (RR = 1.65; 95% CI = 1.15-2.37). Smokers who reported visiting a doctor in the previous 6 months were more likely to quit with assistance versus unassisted compared to those not visiting a doctor, whether they were advised (RR = 1.89, 95% CI = 1.43-2.48) or not advised to quit (OR = 1.32, 95% CI = 1.01-1.74). Similar results were seen when comparing assisted quit attempts with no quit attempts. CONCLUSIONS: Adjusted analyses showed that quitting with assistance was unrelated to measures of SES except ethnic concentration. Physician intervention with patients who smoke is important for increasing assisted quit attempts. IMPLICATIONS: For most measures of SES there were no significant associations with either assisted or unassisted quitting adjusting for demographic and smoking history. Smokers who live in areas with the lowest ethnic concentration were most likely to use assistance as were smokers who visited their doctor and were advised to quit smoking. Interventions to increase the delivery of effective quitting methods in smokers living in areas with high ethnic concentrations and to increase physician compliance with asking and advising patients to quit may increase assisted quit attempts.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".