Litigating the Right to Health: What Can We Learn from a Comparative Law and Health Care Systems Approach
Bibliographic record
Abstract
This article presents research demonstrating that the right to health plays different roles in different types of health systems. In high-income countries with tax-funded health systems, we usually encounter a lack of an enforceable right to heath. In contrast, rights play a more significant role in social health insurance/managed competition systems (which are present in a mixture of high-income and middle-income countries). There is concern, for example in Colombia, that a high volume of rights litigation can challenge the very sustainability of a public health care system and distort resources away from those most in need. Finally, in middle-income countries with big gaps between a poor public health system and a rich private one, we are more likely to find an express constitutional right to health care (or one is inferred from, for example, the right to life). In some of these countries, constitutional rights were included as part of the transition to democracy and an attempt to address huge inequities within society. Here the scale of health inequities suggests that courts need to be bolder in their interpretation of health care rights. We conclude that in adjudicating health rights, courts should scrutinize decision-making through the lens of health equity and equality to better achieve the inherent values of health human rights.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.014 | 0.046 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".