A Comparative Study of Population Health in the United States and Canada during the Neoliberal Era, 1980–2008
Bibliographic record
Abstract
This article draws on the vast evidence that suggests, on one hand, that socioeconomic inequalities in health are present in every society in which they have been measured and, on the other hand, that the size of inequalities varies substantially across societies. We conduct a comparative case study of the United States and Canada to explore the role of neoliberalism as a force that has created inequalities in socioeconomic resources (and thus in health) in both societies and the roles of other societal forces (political, economic, and social) that have provided a buffer, thereby lessening socioeconomic inequalities or their effects on health. Our findings suggest that, from 1980 to 2008, while both the United States and Canada underwent significant neoliberal reforms, Canada showed more resilience in terms of health inequalities as a result of differences in: (a) the degree of income inequality, itself resulting from differences in features of the labor market and tax and transfer policies, (b) equality in the provision of social goods such as health care and education, and (c) the extent of social cohesiveness across race/ethnic- and class-based groups. Our study suggests that further attention must be given to both causes and buffers of health inequalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".