Criminal liability for improper performance of professional duties by a medical professional in the criminal legislation of Ukraine and certain States: a comparative legal study with due regard for international standards and practice
Bibliographic record
Abstract
The purpose of this article is to study and analyze the criminal liability of healthcare professionals based on national and foreign experience. The author analyzes the national legislation of the States in the field of regulation of criminal liability of medical professionals, and identifies the existing achievements and problems. The author analyzes the case law of the European Court of Human Rights on positive obligations of the State in cases involving the proper provision of services by healthcare professionals. Using the comparative legal method of research, the author compares the legal norms regulating the issue of liability for failure to provide or improper provision of medical care, namely, the legislation of Ukraine, Latvia, Poland, Slovenia, Romania, Croatia, Georgia, Armenia, Uzbekistan, Kyrgyzstan, Azerbaijan, Kazakhstan, France, Estonia, Germany, Spain, Canada, Japan and others. A legal analysis of possible options for the process of transferring and implementing the relevant legal provisions from foreign legislation to national legislation through legal mechanisms and methods of bringing a medical professional to criminal liability for improper performance of his/her professional duties is crucial for determining approaches to improving the legal framework of Ukraine regarding the criteria of liability of a medical professional for improper performance of his/her professional duties. It is established that due to the different structure of health care systems in each State and different concepts of legal regulation, approaches to addressing the issue of criminal liability of medical professionals for professional crimes differ primarily in that in most States, such entities are liable for causing harm to life and health of a person, while in the legislation of other States such acts are provided for in a separate chapter or section dealing directly with criminal offenses in the medical field It is determined that foreign countries widely use medical insurance against improper treatment as a guarantee mechanism for compensating patients for harm caused by medical error, which helps to protect violated rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".