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Record W7134336236

Sources of financing post-war reconstruction of the health care system (general terms)

2024· article· uk· W7134336236 on OpenAlexaboutno aff
Віталій Іванович Юнгер

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2024
Typearticle
Languageuk
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careGeneral partnershipContext (archaeology)UkrainianFinancial securityFinancial instrumentMedical careFinancial management
DOInot available

Abstract

fetched live from OpenAlex

The complexity of financial support for post-war reconstruction is associated not only with the search, accumulation and use of financial resources, but also with the need to transform the entire system of financing the activities of health care institutions, training medical and administrative personnel, and providing all types of medical care. It is determined that the structure of the financial mechanism for post-war reconstruction of the health care system includes financing post-war reconstruction of the health care system and the development of a new mechanism for financing the health care system. Priority external sources of financing for post-war reconstruction of the health care system are identified: arrested funds of Russian financial institutions and assets of persons involved in the full-scale invasion of the aggressor country; assistance from international financial organizations, which can be carried out using various instruments; the issue of international support for Ukraine in its post-war recovery is expanding in the context of assistance from foreign countries, in particular the USA, Canada, the European Union countries and others; involvement of international transnational corporations in financial and other assistance in the recovery of the Ukrainian economy as a whole and the healthcare system in particular; attraction of funds from business structures, i.e. legal entities of residents of Ukraine. Instruments for attracting financial resources have been identified, in particular: the creation of the Development Fund of Ukraine; the creation of charitable organizations and the organization of charitable events at the international level to raise funds; the introduction of public-private partnership projects; project and grant activities from the position of individual healthcare entities; privatization, which can be carried out through the introduction of relevant amendments to the legislation on privatization and regulation of the healthcare system. Directions for the recovery of the healthcare system have been identified, namely: the restoration of infrastructure and the restoration of medical services in deoccupied territories and territories of high risk of danger.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.041
GPT teacher head0.283
Teacher spread0.241 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogySame topicEconomic Issues in UkraineFrench-language works237,207