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Record W2045567063 · doi:10.12927/whp.2013.22259

Privatization and Management Development in the Healthcare Sector of Georgia

2011· article· en· W2045567063 on OpenAlexvenueno aff
Daniel J. West, Michael Costello, Bernardo Ramirez

Bibliographic record

VenueWorld health & population · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Socioeconomic and Political Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careAdministration (probate law)Healthcare policyPolitical scienceHealth administrationHealth policyNursing researchPeer reviewUnit (ring theory)Health services researchNursingPublic administrationManagementMedicinePublic relationsInternational healthPsychologyLaw

Abstract

fetched live from OpenAlex

Healthcare reforms in Georgia parallel some of the major changes made by other Central and Eastern European countries. This is especially true of efforts to privatize the health sector and secure capital investments from Western Europe. Privatization of Georgian healthcare requires an understanding of the Soviet-era healthcare system and ideological orientation. Many of the issues and problems of privatization in Georgia require new knowledge to enhance equity outcomes, improve financial performance, increase access to care and encourage healthcare competition. Training existing and future healthcare leaders in modern management theory and practice is paramount. A university based health-management education partnership model was developed and implemented between several universities in the United States and Europe, along with two Georgian universities, to address workforce demands, changing market conditions, management knowledge and leadership competencies. Health-management education concentrations were developed and implemented along with several short courses to meet market demand for trained leaders and managers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.261
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2011
Admission routes1
Has abstractyes

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