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Record W2265720391

A Discussion on Alternative Reimbursement Schemes and Healthcare Utilization in Turkey (Türkiye Özelinde Alternatif Ödeme Sistemlerinin Sağlık Hizmet Sunumuna Etkileri)

2007· article· tr· W2265720391 on OpenAlexaff
Nazmi Sari

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languagetr
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsReimbursementTurkishPaymentProspective payment systemHealth careBusinessMedicineFinanceEconomicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Alternative options in provider reimbursement schemes are being considered to reform the Turkish healthcare system. Recently a prospective payment method based on diagnosis-related groups is offered as a reimbursement alternative for inpatient treatments. Since hospitals have the largest share in total healthcare spending, any reform in hospital reimbursement schemes should be evaluated carefully due to its overall impact on total healthcare expenditure in Turkey. This paper reviews the existing reimbursement schemes in Turkey, and provides a detailed discussion on the prospective payment method based on diagnosis-related groups and its impact on cost efficiency in inpatient treatments. The paper suggest a mixed system as an alternative to either system in achieving efficiency and controlling costs in Turkish hospital markets.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.056
GPT teacher head0.320
Teacher spread0.264 · 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 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
Published2007
Admission routes1
Has abstractyes

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