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Record W2405163412 · doi:10.5539/gjhs.v9n1p201

Analysis of Health and Drug Access Associated with the Purchasing Power of the Ecuadorian Population

2016· article· en· W2405163412 on OpenAlexvenueno aff
Esteban Ortiz‐Prado, J. Millan Ponce, Fernando Cornejo-Leon, Anna M. Stewart‐Ibarra, Aquiles R. Henríquez-Trujillo, Estefanía Espín, Darío Ramírez

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing powerPovertyGovernment (linguistics)Access to medicinesBusinessHealth carePopulationDecileCensusRural areaPurchasingEnvironmental healthSocioeconomicsGeographyEconomic growthMedicineEconomicsDeveloping countryMarketing

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether there is a relationship between access to health systems and out-of-pocket spending with socio demographic characteristics in Ecuador.METHODS: Retrospective analysis of national level data on household medical expenditure from the National Survey of Household Income and Expenditure in Urban and Rural Houses conducted by the Ecuadorian National Institute of Statistics and Census Databases as well as other scientific, institutional, technical-administrative datasets.RESULTS: Families in the lowest percentile of poverty spend proportionally more out-of-pocket on pharmaceutical drugs than wealthier families. Furthermore, the lowest income deciles have no access to private health coverage. Populations from the bigger cities have more access to health care services than smaller rural cities. In Ecuador, 71% of pharmaceutical products are imported and 8% of the total of drugs are generic.CONCLUSIONS: Despite efforts by the current government, health access remains uneven, as indicated by drug access and out-of-pocket expenses per family. Poorer families have higher relative health expenditures for drugs than families with higher incomes, although poorer families have no access to private insurances.

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)0.007
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 score0.018
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.307
Teacher spread0.273 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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