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Record W3103211131 · doi:10.5539/jpl.v13n4p99

Constitutional Right to Health Protection and Medical Care in Ukraine in the Context of the Pandemic COVID-19

2020· article· en· W3103211131 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationContext (archaeology)Right to healthHealth careUkrainianPandemicPolitical scienceData Protection Act 1998Coronavirus disease 2019 (COVID-19)LawMedicineDiseaseGeography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.342
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it