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Record W2547471917 · doi:10.21149/spm.v58i5.7827

Financiamiento, organización, costos y calidad de la atención en los sub-sistemas de salud argentinos

2016· article· es· W2547471917 on OpenAlexaff
Natalia Yavich, Ernesto Báscolo, Jeannie Haggerty

Bibliographic record

VenueSalud Pública de México · 2016
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsSt Mary's Hospital CentreMcGill University Health CentreMcGill University
Fundersnot available
KeywordsBusinessSocial securityHealthcare systemQuality (philosophy)Health servicesHealth careIndustrial organizationWelfare economicsFinanceEconomicsEnvironmental healthEconomic growthMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE:: To analyze the relationship between health system financing and services organization models with costs and health services performance in each of Rosario's health sub-systems. MATERIALS AND METHODS:: The financing and organization models were characterized using secondary data. Costs were calculated using the WHO/SHA methodology. Healthcare quality was measured by a household survey (n=822). RESULTS:: Public subsystem:Vertically integrated funding and primary healthcare as a leading strategy to provide services produced low costs and individual-oriented healthcare but with weak accessibility conditions and comprehensiveness. Private subsystem: Contractual integration and weak regulatory and coordination mechanisms produced effects opposed to those of the public sub-system. Social security: Contractual integration and strong regulatory and coordination mechanisms contributed to intermediate costs and overall high performance. CONCLUSION:: Each subsystem financing and services organization model had a strong and heterogeneous influence on costs and health services performance.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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