Literature Review and Mapping Analysis of the Economic Factors Contributing to Universal Healthcare Coverage in BRIC Countries
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine the economic factors and characteristics of universal healthcare development among Brazil, Russia, India and China (BRIC). METHODS: A policy review was used to achieve this objective. This review established a comparative criterion of the key factors and characteristics of universal healthcare coverage development. Further, a comparison of four countries with established universal healthcare coverage, comprising of each type of healthcare system model, was undertaken against BRIC healthcare systems. The decided upon factors and characteristics of developing and BRIC countries were used to inform and understand the development of process of universal healthcare coverage. RESULTS: The analysis found that continual economic growth and investment into the healthcare coverage, are essential to successful universal healthcare coverage implementation and expansion. CONCLUSION: Understanding the models of healthcare systems along with the key economic factors and characteristics provides important context and understanding into the processes and mechanisms that drive successful universal healthcare coverage in developing countries. The factors and characteristics presented in this study provide a preliminary framework for understanding the conditions that contribute to universal healthcare coverage. Further, this framework can be used as a template for a critical comparison and analysis that can be applied to all high, middle- and low-income countries in their effort to establish universal healthcare coverage.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.026 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".