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Record W1528059908

Household spending and impoverishment

2012· article· en· W1528059908 on OpenAlexaboutno aff
Felícia Marie Knaul, Rebeca Wong, Héctor Arreola‐Ornelas, Fundación Mexicana para la Salud

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansPopulationEconomic growthSocial protectionHealth careCatastrophic illnessDevelopment economicsBusinessGeographyFinanceEconomicsPolitical scienceEnvironmental healthMedicine
DOInot available

Abstract

fetched live from OpenAlex

Among the most serious challenges facing health systems in lower and middle income countries is establishing efficient, fair, and sustainable financing mechanisms that offer universal protection. Lack of financial protection forces families to suffer the burden not only of illness but also of economic ruin and impoverishment. In Latin America, financial protection for health continues to be segmented and fragmented; health is mainly financed through out-of-pocket payments.Financing Health in Latin America presents new and important insight into the crucial issue of financial protection in health systems. The book analyzes the level and determinants of catastrophic health expenditures among households in Argentina, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Mexico, and Peru, applying both descriptive and econometric analyses. The results demonstrate that out-of-pocket health spending is pushing large segments of the population into impoverishment and that the poorest and most vulnerable segments of the population are most at risk of financial catastrophe. This work is a product of the collaboration between more than 25 researchers and 18 institutions associated with the Research for Health Financing in Latin America and the Caribbean Network, with support from the International Development Research Centre of Canada.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.031
GPT teacher head0.236
Teacher spread0.206 · 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 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

Citations7
Published2012
Admission routes1
Has abstractyes

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