Equality, Trust and Universalism in Europe, Canada and the United States: Implications for Health Care Policy
Bibliographic record
Abstract
A number of theoretical explanations seek to describe the factors that have led to the position of the United States as the last industrialized Western nation without a universal health care program. Theories focus on institutional arrangement, historic precedent, and the influence of the private sector and market forces. This study explores another factor: the role of underlying social values. The research examines differences in values among ten European countries, the United States and Canada, and analyzes the associations between the values that have been seen to contribute the individualism-collectivism dynamic in the United States. The hypothesis that equality and generalized trust are positively associated with universalism is only partially true. Equality is positively associated (B = .301, p < .001), while generalized trust is negatively associated with universalism (B = -.052, p < .001). Not only do Americans show lower levels of support for income equality and universalism than Europeans, but the effect of being American holds even after controlling for socio-demographic and religious variables (B = .044, p < .01). When the model tests the association of equality and trust on universalism in each region, it explains approximately 17 percent of the variance of universalism for the United States, and approximately 13 percent in Europe and Canada.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".