Understanding the social patterning of smoking practices: a dynamic typology
Bibliographic record
Abstract
In many countries, cigarette consumption has been on a declining trend for over 20 years. However, different patterns of smoking practices have emerged. Our goal is to explore how the patterning of smoking practices occurs and persists over time, and to investigate the factors that could help interpret these patterns. Data were derived from the National Population Health Surveys and comprise a large representative sample of the population. Dynamic Typology methods reveal two main classes of typology: monothetic groups with stable patterns of behaviour over time (never-smokers, chronically addicted smokers, long-term ex-smokers); and polythetic groups with substantial behavioural variations. Moreover, socioeconomic inequalities are found among all groups, and gender-specific clusters of behaviour become apparent, with specific risk groups, such as the group of young women aged 20-24 at risk of becoming highly addicted. Our results also show that the effects of socioeconomic position on smoking practices are not significantly mediated by psychosocial variables such as self-esteem and personal control in both females and males. However, these variables still exert independent and differential effects on smoking practices in both genders. Our findings indicate that analysis of temporal patterns of smoking is crucial for tailoring type and timing of health-promoting interventions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".