Subjective well-being, personality, demographic variables, and American state differences in smoking prevalence
Bibliographic record
Abstract
INTRODUCTION: The present study was conducted to determine relations between smoking prevalence, subjective well-being, and the Big Five personality variables at the American state level. METHOD: State smoking prevalence was based on the responses of more than 350,000 adults interviewed in the Behavioral Risk Factor Surveillance System in 2008. Subjective well-being was based on the state-aggregated responses of 353,039 adults to the Gallup-Healthways Well-Being Index phone interviews during 2008. Big Five variables were based on the state-aggregated responses of 619,397 persons to an Internet survey between 1999 and 2005, which included the 44-item Big Five Inventory. RESULTS: Well-being and smoking prevalence were negatively correlated and remained so when state Big Five, socioeconomic status (SES), White population percent, urban population percent, and median age were controlled in a partial correlation. Hierarchical and stepwise multiple regressions showed (a) that SES and neuroticism were the prime predictors of well-being, (b) that well-being was the prime predictor of smoking prevalence, and (c) that openness to experience was the sole personality or demographic variable to account for differences in smoking prevalence when well-being was controlled, and it explained very little of the remaining variance. DISCUSSION: Applied implications for state-tailored attempts to reduce smoking are briefly discussed, and suggestions for future research directions are put forward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".