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Record W2982630150 · doi:10.5430/ijhe.v8n7p29

Teaching the Public Administration in Health Care in the Russian Federation

2019· article· en· W2982630150 on OpenAlexvenueno aff
Timur Giorgievich Okriashvili, Bert Valentinovich Pavlyuk, A.V. Smyshlyaev, Albert Gumarovich Yakupov

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldMedicine
TopicEmergency Medicine Education and Research
Canadian institutionsnot available
FundersKazan Federal University
KeywordsLegislationRussian federationState (computer science)Administration (probate law)Public administrationHealth careWork (physics)Political scienceSocial spherePublic spherePublic healthMedical carePublic relationsLawMedicineSociologyNursingSocial sciencePoliticsEngineeringComputer scienceRegional science

Abstract

fetched live from OpenAlex

Medicine is an important component of the stability of any state, so the study of teaching the legal problems and state regulation of this sphere is useful from both theoretical and practical points of view. Currently, the health protection of the country is one of the priorities of the state policy in the social sphere, which is vividly demonstrated in the “May” decrees of the President of the Russian Federation from 2012 and 2018. Thus teaching the health care financing is the most important instrument of state regulation of this sphere of public life. The article analyzes the main tasks and directions of development of state support for the health care system in recent years. That also allowed to identify the most acute problems. This work may be useful for developing proposals for improving the relevant legislation in this area. In addition, this article will be useful for representatives of both medical and legal science. In the future, it is necessary to improve scientific cooperation between representatives of legal and medical Sciences.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.466

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.445
Teacher spread0.416 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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