The Influence of Proverty, Deprivation and Social class on Health inequality: a review of American and Canadian Literature/La Influencia de la Pobreza, de la Privación y de la Clase Social en la Desigualdad de la Salud: Una revisión de la Literatura Americana y Canadiense
Bibliographic record
Abstract
The need to understand inequalities in health cannot be limited to the study of differences in healthcare based upon income. One must consider the other elements of social status. Studying of these elements allow a better understanding of the difficulties in daily living which the poorest must face. In this article we present a comparative analysis of the literature concerning health inequalities in the United States and Canada. In a first part we look at the differences in health based upon income, showing that income differences have a greater impact on health in the United States than in Canada. In a second part we complete the analysis by discussing four other elements of social status. These allow us to understand how social inequalities in health develop in a territory. In a third part we briefly describe the health systems of the two countries with emphases on programs to care for disadvantaged people. We conclude that the relations between social position and health and the political implication thereof, should be analyzed in a broader sense that just reform of the healthcare system. La necesidad de entender las desigualdades en la salud no se puede limitar al estudio de diferencias en el cuidado de la salud en función del ingreso. Se deben considerar otros elementos del status social. En este artículo presentamos un anaálisis comparativo de la literatura referente a desigualdades en la salud en los Estados Unidos y Canadá. En una primera parte, se consideran las difernecias en la salud basadas en el ingreso, demostrando que las diferencias de la renta tienen un mayor impacto en salud en los Estados Unidos que en Canadá. En una segunda parte, se completa el análisis discutiendo otros cuatro elementos del status social, los cuales nos permiten entender cómo las desigualdades sociales en la salud se desarrollan geográficamente. En un tercer apartado, se describe el sistema de salud de los dos países con énfasis en los programas de cuidados para personas discapacitadas. En conclusión, las relaciones entre status social y salud e implicaciones políticas deberían ser analizadas en un sentido amplio que lleve a una reforma igualitaria del sistema de salud.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.036 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".