Time perspective as a predictor of smoking status: findings from the International Tobacco Control (ITC) Surveys in Scotland, France, Germany, China, and Malaysia
Bibliographic record
Abstract
BACKGROUND: Prior studies have demonstrated that time perspective-the propensity to consider short-versus long-term consequences of one's actions-is a potentially important predictor of health-related behaviors, including smoking. However, most prior studies have been conducted within single high-income countries. The aim of this study was to examine whether time perspective was associated with the likelihood of being a smoker or non-smoker across five countries that vary in smoking behavior and strength of tobacco control policies. METHODS: The data were from the International Tobacco Control (ITC) Surveys in five countries with large probability samples of both smokers (N=10,341) and non-smokers (N=4,955): Scotland, France, Germany, China, and Malaysia. The surveys were conducted between 2005-2008. Survey respondents indicated their smoking status (smoker vs. non-smoker) and time perspective (future oriented vs. not future-oriented) and provided demographic information. RESULTS: Across all five countries, non-smokers were significantly more likely to be future-oriented (66%) than were smokers (57%), χ(2)(1, N = 15,244) = 120.64, p < .001. This bivariate relationship between time perspective and smoking status held in a multivariate analysis. After controlling for country, age, sex, income, education, and ethnicity (language in France), those who were future-oriented had 36% greater odds of being a non-smoker than a smoker (95% CI: 1.22 to 1.51, p<.001). CONCLUSION: These findings establish time perspective as an important predictor of smoking status across multiple countries and suggest the potential value of incorporating material to enhance future orientation in smoking cessation 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".