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Record W2983866971 · doi:10.3390/soc9040077

Healthcare Financing in Rural Cameroon

2019· article· en· W2983866971 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

VenueSocieties · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsYork University
Fundersnot available
KeywordsHealth careReciprocity (cultural anthropology)BusinessLow incomeHealth care financingFinancePublic economicsEconomic growthDemographic economicsEconomicsSociology

Abstract

fetched live from OpenAlex

In the global South, low-income populations are faced with frequent health shocks. Formal mechanisms to protect them against these shocks are absent or limited. Thus, what are the mechanisms used by low-income rural populations to finance healthcare? This paper draws on a qualitative study to examine the healthcare financing mechanisms of low-income rural populations in Cameroon. The findings suggest that low-income populations use 13 mechanisms to finance healthcare. Depending on several factors, people may use more than one of these mechanisms. In addition, social factors shape people’s patterns of usage of these mechanisms. Patterns of usage of these mechanisms are embedded in the principle of reciprocity. The notion of reciprocity does seem to discourage people from enrolling in the limited voluntary health insurance programmes which exist in various communities. Newly insured people were more likely to drop out if they did not receive a payout.

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)0.000
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 score0.007
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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