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Record W2061894294 · doi:10.1071/ah020052

The effects of increased market competition on hospital services in Shandong and Henan Provinces

2002· article· en· W2061894294 on OpenAlexaff
Ian Forbes, Don Hindle, Pieter Degeling, Kai Zhang, Lingzhong Xu, Qingyue Meng, Jian Wang

Bibliographic record

VenueAustralian Health Review · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCompetition (biology)Government (linguistics)BusinessProductivityRevenueSustainabilityPublic economicsEconomic growthFinanceEconomics

Abstract

fetched live from OpenAlex

The Chinese government began a major reform of the hospital sector in the early 1980s. The main aim was to increase productivity by phasing out prospective global budgets from the government, and encouraging between-hospital competition for the business of user-pay and insured patients. This goal was to be achieved without unreasonable prejudice to the financial sustainability of hospitals or to the fairness of access and service provision. We explored the effects of these changes by analysing data for four levels of hospital in two of the most populous provinces between 1985 and 1999. We used data envelope analysis, and found that the majority of hospitals experienced a decline in productivity. Social efficiency (measured by the level of provision of unnecessary services) also declined, especially in the largest hospitals that could easily increase the use of expensive technologies. Most hospitals increased their economic sustainability, measured as the ratio between revenue and expenditures. However, the lowest-level hospitals experienced stable or reduced sustainability due to their inability to compete with marketing by higher-level hospitals. We conclude that, although there were many benefits, the overall impact of the introduction of market forces may have been negative. An important factor was that not all aspects (such as supplier-induced demand) were adequately controlled by government agencies. We suggest ways of alleviating the most problematic elements of current arrangements.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.697
Threshold uncertainty score0.788

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 integrity0.0000.000
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 head0.281
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2002
Admission routes1
Has abstractyes

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