Constitutional Right to Health Protection and Medical Care in Ukraine in the Context of the Pandemic COVID-19
Bibliographic record
Abstract
The content of the right to health protection and medical care according to Ukrainian legislation is analyzed in the article as well as peculiarities of its realisation in the context of the pandemic COVID-19. It examines also the correlation between the notion “health protection” and “medical care”. On the basis of this correlation, the conclusion is made that the right to health protection is broader and includes, but is not limited to, the right to medical care. Some international standards in the sphere of health protection, which constitute the basis of Ukrainian legislation in this area, are analyzed. The conclusion is made that Ukraine should take into account such standards while limiting human rights, in particular, the right to health protection and medical care in the context of the pandemic COVID-19. It is mentioned that the significant problem remains the legal regulation of quality control of medical care, the creation of organizational technologies with a clear division of control functions between the various actors in the health care system, which is extremely important in terms of the pandemic. The attention is also paid to the personal data protection issue in the sphere of health care. The conclusion is drawn that there should be mechanisms for reporting and protecting against abuse while collecting personal data, and people should be able to challenge any COVID-19-related measures for the collection, aggregation, storage and further use of their data.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".