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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".