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Record W2075593078 · doi:10.5430/jha.v4n1p1

Healthcare financing in Nigeria: A systematic review assessing the evidence of the impact of health insurance on primary health care delivery

2014· review· en· W2075593078 on OpenAlexvenueno aff
Ejughemre Ufuoma John, Agada-Amade Y. A, Oyibo P.G, Chukwuebuka Ugwu

Bibliographic record

VenueJournal of Hospital Administration · 2014
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careBusinessQuality (philosophy)Health insuranceHealth policyFinanceActuarial scienceMedicineEconomic growthEconomics

Abstract

fetched live from OpenAlex

Strengthening health systems, improving health outcomes, as well as finding answers to the competing alternatives of healthcare financing are critical issues that continue to bother health policy makers. Irrespective of the options, the choice of health care financing should mobilize resources for health and improve access to quality care at the same time. Notably, the health financing policy in Nigeria provides a framework for establishing health insurance schemes so as to expand coverage in health care delivery for the formal and informal sectors as a strategy towards universal access to healthcare. Accordingly, the authors, through this review, systematically assess the evidence of the extent to which health insurance impacts on access to services and quality of primary healthcare in Nigeria. While this comes to bear, the findings reveal an evidence of moderate-to-high strength that health insurance increases access to care and improves the quality of care received; however, it remains equivocal in some instances. The review therefore contributes to the literature on healthcare financing by extending and qualifying existing knowledge and advocating for accelerated reforms if universal coverage will be achieved.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.295
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.371
Teacher spread0.313 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2014
Admission routes1
Has abstractyes

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