The Debatable Role of Courts in Brazil's Health Care System: Does Litigation Harm or Help?
Bibliographic record
Abstract
Recent studies of the Brazilian case suggest that successful litigation can have regressive effects and negatively impact the health care system. While the data to support this claim is not conclusive, this paper assumes that such immediate regressive effects are indeed taking place, but asks if these are the only consequences that should be analyzed in assessing the impact of right to health litigation in Brazil. The answer is no. The current perspective adopted to assess right to health litigation in Brazil is too narrow. Other consequences can and should be considered in analyzing the overall impact of litigation. To go beyond the set of questions asked by the existing experts on the topic, this paper analyzes whether the right to health litigation in Brazil has the potential, and could be generating: (i) policy changes within the health care system; (ii) institutional changes within the health care system; and (iii) institutional changes outside the health care system. After presenting anecdotal evidence that suggests these three types of changes may be happening in Brazil, I conclude the paper by discussing what would be required to assess them, and how these changes may affect our overall assessment of the more immediate and supposedly negative impact that litigation has had on the system.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".