"Hardcore" definitions and their application to a population-based sample of smokers
Bibliographic record
Abstract
INTRODUCTION: As smoking prevalence declines, some suggest that so-called "hardcore" smokers will come to represent a growing and irreducible proportion of current smokers ("hardening hypothesis"). Different definitions of a "hardcore" smoker have been used in the literature. This paper describes population-based definitions of "hardcore" smokers and compares estimates of the prevalence of "hardcore" smokers derived using these definitions. METHODS: Definitions identified in a comprehensive literature search were reduced to their component constructs. We estimated the prevalence of "hardcore" smokers as a proportion of all current adult smokers in Ontario, Canada, using data from the Ontario Tobacco Survey (2005-2008; N = 4,130). Definition concordance was examined using bivariate cross-tabulations. RESULTS: Six definitions were identified in the literature. Five definitions included constructs of quit intentions and quit attempts, four included nicotine dependence, three included long-term use, and one included a measure of smoker knowledge about smoking hazards and confronting substantial societal disapprobation of smoking. Estimates of "hardcore" smoker prevalence in Ontario based on these definitions ranged from 0.03% to 13.77%. CONCLUSIONS: Estimates of the prevalence of "hardcore" smokers in Ontario varied considerably between the six definitions of the "hardcore" smokers found in the population-based literature. This study underscores the need for consensus on the best definition of "hardcore" smoker.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".