Application of Restorative Justice Values in Settling Medical Malpractice Cases
Bibliographic record
Abstract
Lawsuits submitted by patients or their families to the hospital and / or their doctors can take the form of criminal or civil lawsuits by almost always basing on the theory of negligence. This paper seeks to explore the application of the values of restorative justice in resolving cases of medical malpractice in Indonesia. This research is a qualitative research using normative legal research and uses a statute approach and a conceptual approach. The results showed that settlement of medical malpractice cases through a restorative justice approach or which is known in the culture of the Indonesian people as a consensus agreement as contained in the 4th Precepts of Pancasila is one alternative settlement that is to restore conflict to the parties most affected (victims, perpetrators and interests community) and give priority to the interests of all parties. The conclusion showed that the restorative justice emphasizes human rights and the need to recognize the impact of social injustice and in simple ways to restore the parties to their original condition rather than simply giving formal justice actors or legal actors and victims not getting any justice. Hence, restorative justice also strives to restore the security of victims, personal respect, dignity and more importantly is a sense of control so as to avoid feelings of revenge both individual or family or group.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".