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Record W2153250552 · doi:10.18235/0012535

From Few to Many: Ten Years of Health Insurance Expansion in Colombia

2009· article· en· W2153250552 on OpenAlexaboutno aff
Antonio Giuffrida, Carmen Elisa Flórez, Úrsula Giedión, Enriqueta Cueto, Juan Gonzalo López, Amanda Glassman, Ramón Abel Castaño, Diana M. Pinto, Renata Bonini Pardo, Teresa M. Tono, William D. Savedoff, Eduardo Andrés Alfonso, Leslie F. Stone, Álvaro López, Beatriz Yadira Díaz, María Luisa Escobar, Carlos H. Arango, Fernando Ruíz Gómez, Olga Lucía Acosta

Bibliographic record

VenueInter-American Development Bank eBooks · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersUniversidad del RosarioInter-American Development BankBill and Melinda Gates Foundation
KeywordsSubsidyRecessionHealth careHealth insuranceQuarter (Canadian coin)Economic growthPopulationBusinessHealth policyDevelopment economicsDeveloping countryEconomic policyPolitical scienceEconomicsMedicineGeographyEnvironmental health

Abstract

fetched live from OpenAlex

From Few to Many is the first comprehensive look at Colombia's 1993 health system reforms. It describes the implementation of universal health insurance, including a subsidized system for the poor, and examines the impact of this and other reforms during a time when Colombia experienced crushing recession and internal conflict that displaced half a million people. Prior to the reforms, a quarter of the Colombian population had health insurance. Subsidies failed to reach the poor, who were vulnerable to catastrophic financial consequences of illness. Yet by 2008, 85 percent of the population benefited from health insurance. From Few to Many describes the challenges and benefits of implementing social health reforms in a developing country, exploring health care financing, institutional reform, the effects of political will on health care, and more. The reforms have provided important lessons not only for continued reform in Colombia, but also for other nations facing similar challenges.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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 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

Citations26
Published2009
Admission routes1
Has abstractyes

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