Do components of current ‘hardcore smoker’ definitions predict quitting behaviour?
Bibliographic record
Abstract
AIMS: It has been hypothesized that the smoking population is represented by an increasingly 'hardcore' group of smokers who are resistant to quitting. Many definitions of 'hardcore smokers' have been used, but their predictive validity is unknown. To evaluate whether 'hardcore smoker' definition components predict quitting behaviours and which combinations of 'hardcore' components are most predictive. DESIGN, SETTING AND PARTICIPANTS: Longitudinal, random telephone survey of a representative sample of adult smokers in Ontario, Canada (n = 4130, recruited 2005-08 and followed for 1 year). MEASUREMENTS: Multiple logistic regression models were compared to evaluate the predictive ability of 'hardcore' components (high daily cigarette consumption, high nicotine dependence, being a daily smoker, history of long-term smoking, no quit intention and no life-time quit attempt) on three outcomes [continued smoking, not attempting to quit and having unsuccessful quit attempt(s)]. FINDINGS: All 'hardcore' components predicted having no quit attempt and continued smoking during follow-up (P < 0.05), except for history of long-term smoking and no life-time quit attempt (for continued smoking). Among respondents who made 1 + quit attempts during follow-up, only high nicotine dependence, high daily cigarette consumption and being a daily smoker were predictive of quitting failure (P < 0.01). The best combination of components depended on the outcome. CONCLUSIONS: Measures of 'hardcore' include a mixture of motivational, dependence and behavioural variables. As found previously, motivational and behavioural measures, such as intention to quit, predict failure to make quit attempts. However, dependence components best predicted continued smoking and thus would be best for further exploring the hardening hypothesis.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".