Socio‐economic disadvantage at the area level poses few direct barriers to smoking cessation for Australian smokers: Findings from the International Tobacco Control Australian cohort survey
Bibliographic record
Abstract
INTRODUCTION: Area-level indicators of socio-economic variation are frequently included in models of individual health outcomes. Area disadvantage is linearly related to smoking prevalence, but its relation to cessation outcomes is less well understood. AIMS: To explore the relationship between area-level disadvantage and prospective data on smoking cessation. DESIGN AND METHODS: The Australian cohort of the International Tobacco Control Four-Country Survey (N = 3503) was used to prospectively examine the contribution of area-level socio-economic disadvantage to predicting three important smoking-cessation outcomes: making a quit attempt, achieving 1 month abstinence and achieving 6 month abstinence from smoking, while controlling for individual-level socio-economic indicators and other individual-level covariates related to smoking cessation. RESULTS: Only two independent associations were observed between socio-economic disadvantage and cessation outcomes. Area-level disadvantage was related to 1 month abstinence in a non-linear fashion, and the individual experience of smoking-induced deprivation was associated with a lower likelihood of making quit attempts. DISCUSSION: Despite the documented higher prevalence of smoking among the more disadvantaged and in more disadvantaged areas, socio-economic disadvantage was not consistently related to making quit attempts, nor to medium-term success. Nevertheless, indirect effects of disadvantage, like its impact on psychological distress, cannot be ruled out, and considering smokers' individual psychosocial circumstances is likely to aid cessation efforts. CONCLUSION: Socio-economic disadvantage, particularly at the area level, poses few direct barriers to smoking cessation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".