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Record W2334700582 · doi:10.1068/c1119r

Ghana's National Health Insurance Scheme: Helping the Poor or Leaving Them Behind?

2011· article· en· W2334700582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMemorial University of NewfoundlandWestern University
Fundersnot available
KeywordsNational Health Interview SurveyMandateNational health insuranceHealth insuranceEconomic growthBusinessSocioeconomicsEnvironmental healthMedicinePolitical scienceHealth careEconomicsPopulation

Abstract

fetched live from OpenAlex

We present findings on the determinants of enrolment for Ghana's National Health Insurance Scheme (NHIS). With this study we contribute to the literature by providing one of the few quantitative analyses on a nationwide survey. Using data from the 2008 Ghana Demographic and Health Survey, we find that those from the poorest households remain significantly less likely to enrol in the NHIS compared with respondents from wealthy households, even after controlling for theoretically relevant variables. However, our analysis also shows that respondents in Northern Ghana, considered the poorest part of the country, are more likely to be enroled than those in Southern Ghana. The findings present a clear challenge to the original mandate of the NHIS as a propoor policy and suggest that health policy makers should consider expanding and clarifying the criteria for declaring a person as indigent and that the scheme be further evaluated for obstacles that may be hindering enrolment.

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.

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 score0.058
Threshold uncertainty score0.397

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 head0.089
GPT teacher head0.259
Teacher spread0.169 · 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