Tobacco control’s effects on social inequalities in smoking: Moving towards a health equity approach
Bibliographic record
Abstract
Abstract Population-level public health policies aim to improve health for the entire population. Yet, in doing so, they may unintentionally neglect vulnerable populations' specific needs, which may perpetuate social inequalities in health. As an example, tobacco control policies (e.g. media campaigns, smoke-free places, tax increases, and tobacco product regulation) have been found to significantly reduce overall smoking prevalence in many high-income countries. However, social inequalities in smoking have been increasing, with smoking prevalence being higher, notably for those of low socio-economic status (SES). Low SES individuals start smoking at younger ages, smoke more cigarettes per day, have lower cessation rates, and are exposed to more second-hand smoke than higher SES individuals. These social inequalities in smoking translate into social inequalities in health such that low SES groups carry a disproportionately heavier burden of smoking-related illnesses. Based on data from a critical discourse analysis of tobacco control policy in Quebec, Canada, as well as from a literature review of vulnerable populations' experiences with tobacco control policies, this presentation will: 1) illustrate ways in which tobacco control policies may be increasing social inequalities in smoking, including the absence of vulnerable populations who smoke from policy planning, smoking denormalization's unintended stigmatizing effects, and targeting behaviours rather than the 'causes of the cause' in policy; 2) provide ideas for future population-level policies based on a health equity approach, which includes integrating vulnerable population's voices in policy design, prioritizing vulnerable populations and health equity in policy, and shifting attention towards policies addressing social inequalities in access to social determinants (e.g. education, income, employment security, safe, clean, and affordable housing) to improve health rather than targeting behaviours, such as smoking.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".