MétaCan
Menu
Back to cohort
Record W2808823227 · doi:10.1177/0020731418779508

Performance-based Financing in Africa: Time to Test Measures for Equity

2018· article· en· W2808823227 on OpenAlexafffund
Valéry Ridde, Lara Gautier, Anne‐Marie Turcotte‐Tremblay, Isidore Sieleunou, Élisabeth Paul

Bibliographic record

VenueInternational Journal of Health Services · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversité de Montréal
FundersInstitute of Population and Public HealthCanadian Institutes of Health Research
KeywordsEquity (law)Psychological interventionHealth care financingEconomicsFinancePurchasingHealth carePublic economicsHealth equityBusinessDeveloping countryDevelopment economicsEconomic growthPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Over the past 15 years, hundreds of millions of dollars have been invested in reforms founded on performance-based financing (PBF) in low- and middle-income countries. While evidence on its effectiveness and efficiency is still controversial, there appears to be an emerging consensus that equity has not been adequately considered. In this article, we show how PBF-type interventions in Africa have not sufficiently taken into account equity of access to care for the worst-off and their financial protection. In reviewing the history of health reforms in Africa, we show that this omission is nothing new. We suggest that strategic purchasing and PBF-type actions would benefit from being implemented in ways that promote equity and the financial protection of populations in Africa. Without such a reorientation of reforms, it will be impossible to achieve universal health coverage by 2030.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.053
metaresearch head score (Gemma)0.192
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.192
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0020.007
Scholarly communication0.0050.021
Open science0.0010.006
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.080
GPT teacher head0.330
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Health ServicesSame topicHealthcare Systems and ReformsFrench-language works237,207