US state death rates: Structural equation modeling of Big Five personality, socioeconomic status, and health risk factors
Bibliographic record
Abstract
Structural equation modeling (SEM) tested the plausibility of a causal model with neuroticism, openness to experience, socioeconomic status (SES), and race as predictors of a composite of six health risks and age-adjusted all-cause mortality in 2020 using the 48 contiguous American states as analytic units. In the final model, neuroticism, openness, and SES accounted for 80% of the health risk composite variance. These three variables and composite health risk accounted for 85% of the death rate variance. Neuroticism, openness, and SES had direct impacts on the health risk composite and indirect impacts on death rates through the health risk composite. SES and composite health risk also had direct impacts on death rates. Spatial autocorrelation and multicollinearity were not problematic. These SEM results underline the importance of state resident personality and SES in this context and support the plausibility of the speculation that the demonstrated relations may be causal in nature.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".