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Record W2582827576

Service Quality Perspectives and Satisfaction in Health Care Systems-A study of select hospitals in Hyderabad

2006· article· en· W2582827576 on OpenAlexaboutno aff
Priya Deshpande

Bibliographic record

VenueIndian Journal of Marketing · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessHealth careGlobalizationProductivityCompetition (biology)Economic growthLiberalizationQuality (philosophy)RecessionService (business)MarketingEconomicsMarket economy
DOInot available

Abstract

fetched live from OpenAlex

Liberalization, Privatization and Globalization (LPG) has brought unprecedented changes in the economic, trade and industrial scenario. LPG environment has exposed various organizations including the service sectors to the challenges of competition; service quality, cost and the competitive environment will help organizations to modernize. The impact of globalization and its implications for our country's health care sector has rightly received wide attention and has been the subject of various health conclaves. With the state-of- the art medical procedures, equipment and facilities now available in India, patients from developed countries like Canada and Britain are choosing Indian Hospitals. Today India is not only well poised to meet the health care challenges of the millennium but also equipped with the talent and strength to contribute in further developing the health and economy of the world.Health is Wealth, the old saying still holds true. It is increasingly being recognized that good health is an important contributor to the productivity and economic growth at the same time it is first and foremost and an end itself. Perhaps, health care industry is one industry, which never faces a recession. Entry of private participants in the health insurance will enhance the accessibility of health care facilities to millions thus providing the right kind of health care services at affordable cost.

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)0.008
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: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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
Published2006
Admission routes1
Has abstractyes

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