Wellbeing across the American Nations: First Settler Effects influence traditional and existential wellness
Bibliographic record
Abstract
This study investigates the "First Settler Wellness Effect," exploring how cultural geography impacts traditional wellness (physical health, social relationships, and financial stability) and existential wellness (purpose, meaning, and community identity) across the United States. Using data from the Gallup-Healthways Well-Being Index, which includes responses from over 325,000 individuals across 110 Metropolitan Statistical Areas (MSAs) from 2009 to 2016, we analyze wellness outcomes through the lens of the American Nations Model. This model categorizes the United States into distinct cultural regions shaped by early settlement patterns, emphasizing the enduring influence of regional norms and ideologies. Our findings, in support of the American Nations Model, reveal significant regional variation in wellness outcomes. Northeastern and Midwestern regions, characterized by communal norms, high educational attainment, and institutional trust, exhibit elevated traditional wellness scores (e.g., β = 0.371, p = 0.002). These regions reflect a stability rooted in health infrastructure and economic security. In contrast, Southern regions, shaped by honor-based values emphasizing personal autonomy, loyalty, and social reputation, show significantly higher existential wellness (e.g., β = 0.590, p = 0.011). This divergence highlights a tradeoff between material stability and existential fulfillment shaped by cultural norms. Interestingly, Southern regions demonstrate elevated existential wellness for Black and Hispanic residents compared to other regions, suggesting localized cultural or community support may offset systemic disparities. Conversely, Northeastern and Midwestern regions report higher traditional wellness yet fail to foster similar levels of existential fulfillment, underscoring the limitations of material prosperity alone. These findings emphasize the interplay between cultural history, regional identity, and human flourishing, offering insights for targeted public health and policy 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".