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Record W2494780705 · doi:10.5430/jha.v5n5p53

Equity in healthcare financing in Nigeria

2016· article· en· W2494780705 on OpenAlexvenueno aff
Akanni Olayinka Lawanson, Olaide Sekinat Opeloyeru

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)Health carePaymentInequalityPopulationHealth care financingEconomicsBusinessPublic economicsFinanceEconomic growthPolitical scienceMathematicsSociologyDemography

Abstract

fetched live from OpenAlex

Using both graphical and geometric analyses, this paper examines the extent of inequity in healthcare finance among the Nigerian population. One of the acclaimed plans in healthcare in this teeming community is the mix method of financing but the system in practise is dominated with Object Oriented Programmings (OOPs) meanwhile the degree of income inequality still remain high. Using the Nigerian Living Standard Survey (NLSS) data conducted in 2004, the framework for this study relies on Kakwani Progressivity Index (KPI). A decomposition of the groups into lower and upper bound however, reveal that health payment for lower bound is regressive while payment for upper bound is progressive. Although, the upper bound result dominates the entire result which finally suggests a progressive system arising from the spending habit of the rich in seeking for healthcare services abroad.

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)0.001
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 score0.013
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.036
GPT teacher head0.304
Teacher spread0.267 · 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
Published2016
Admission routes1
Has abstractyes

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