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Record W2405627894

Community-Based Health Financing: CARE India's Experience in the Maternal and Infant Survival Project

2002· article· en· W2405627894 on OpenAlexaff
Siddharth Agarwal, Irene Sarasua

Bibliographic record

VenueSocial Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsMcGill University
Fundersnot available
KeywordsEconomic growthMaternal healthBusinessHealth care financingHealth careFinanceMedicineNursingPolitical scienceEnvironmental healthEconomicsHealth services
DOInot available

Abstract

fetched live from OpenAlex

In a rural Indian population beset with inadequate health access due to socio-cultural and economic factors, CARE India under the Maternal and Infant Survival Project encouraged village women to form Community Based Organizations (CBOs) and to save health funds collectively. After 15 months of implementation, CBOs were formed in 345 of 447 project villages and health funds were operational in 203 villages. A total of 292 persons benefited from health funds through loans for treatment of obstetric complications and infant illnesses. Additional initiatives include social marketing, sales of disposable delivery kits, and village drug banks. Over half (56 percent) of the loans were repaid within the grace/low interest period. This experience demonstrates that village women, when appropriately encouraged, are capable of creating rules and managing health funds. The process empowers village women (through access to resources and information and the strength of social capital) to make decisions and act to improve their well being.

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)0.005
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.005
Scholarly communication0.0060.002
Open science0.0020.009
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.260
GPT teacher head0.426
Teacher spread0.167 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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