Sistemas de salud en condiciones de mercado: las reformas del último cuarto de siglo* / Health systems under market conditions: the reforms carried out during the last quarter century
Bibliographic record
Abstract
Objetivo: el ensayo explora los cambios a partir de la introducción de los mecanismos del mercado en las reformas del último cuarto de siglo. Metodología: se toma como eje analítico las condiciones de mercado, determinante de las configuraciones y de los resultados que presentan actualmente los sistemas de salud en la mayoría de los países. Se complementa con el análisis de la presencia o no de mecanismos de regulación que permitan reducir el impacto negativo de las imperfecciones del mercado sobre la salud. Discusión: el artículo parte de caracterizar varios tipos de mercados de servicios de salud y considera, de otro lado, la función estatal, y su articulación en la mezcla público- privado, luego de analizar algunas tipologías de los sistemas de salud relacionadas con los modelos de mercado. Conclusión: los mecanismos del mercado introducidos en las reformas de salud en el último cuarto de siglo en la mayoría de países de América Latina (al) han transformado los servicios de salud en favor del mercado financiero transnacional, y han generado inequidad, ineficiencia, corrupción, desequilibrio financiero del sistema de salud, y malogrado las condiciones de salud de la población Objective: the essay explores the changes based on market mechanisms introduced in the reforms of the last quarter century. Methodology: market conditions are taken as an analytical axis determining the configurations and results of health systems seen today in the majority of countries. The analysis is complemented by the presence or absence of regulatory mechanisms which reduce the negative impact of market imperfections on health. Discussion: this paper aims to characterize various health care market types while taking into account the function of the State, and its articulation within the public-private sector after analyzing some of the types of health system that are related to the market models. Conclusion: the market mechanisms introduced in the health reforms in the last quarter century in most Latin American countries have transformed health services in favor of the transnational financial market, thus generating inequity, inefficiency, corruption and financial imbalance in the health system, which in turn has a negative effect in the health conditions of the population
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".