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Record W2617105768

Results-based financing : evidence from performance-based financing in the health sector

2013· preprint· en· W2617105768 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEconstor (Econstor) · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
FundersDeutsches Institut für EntwicklungspolitikDanish International Development AgencyDepartment for International DevelopmentWorld Health OrganizationEuropean CommissionStyrelsen för Internationellt UtvecklingssamarbeteUnited States Agency for International DevelopmentGlobal Fund to Fight AIDS, Tuberculosis and MalariaGAVI AllianceOverseas Development InstituteMcGill UniversityUnited Nations Population Fund
KeywordsIncentiveBusinessFinanceHealth careHealth sectorDeveloping countryHealthcare systemPublic economicsEconomicsEconomic growthMedicineHealth services
DOInot available

Abstract

fetched live from OpenAlex

Results-based approaches have been a focus of recent discussions in international development. This paper discusses if performance-based financing (PBF) can make foreign and domestic funding in the health sector more effective. It studies the experiences and data from PBF programmes in 13 developing countries in Africa, Asia and South America and evaluates their targeting mechanisms, incentive structure, effectiveness and efficiency. It finds that PBF may improve the effectiveness of healthcare supply and healthcare coverage, but that more monitoring and research are needed to evaluate its full potential. In the future research agenda, efforts should particularly focus on investigating the incentive structure of RBF more thoroughly – including non-monetary and perverse incentives –, on evaluating the effectiveness and efficiency of schemes more rigorously, and on studying the long-term effects of RBF.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.068
GPT teacher head0.265
Teacher spread0.196 · 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