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Record W2086364656 · doi:10.1186/s12916-014-0184-4

How do you circumcise a nation? The Rwandan case study

2014· article· en· W2086364656 on OpenAlexaff
Vincent Mutabazi, Jamie I. Forrest, Nathan Ford, Edward J. Mills

Bibliographic record

VenueBMC Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicGenital Health and Disease
Canadian institutionsInstitute for Clinical Evaluative Sciences
FundersWorld Health Organization
KeywordsMedicineOptimismScale (ratio)Human immunodeficiency virus (HIV)Male circumcisionDeveloping countryPublic relationsEconomic growthEnvironmental healthFamily medicineHealth servicesPopulation

Abstract

fetched live from OpenAlex

Voluntary medical male circumcision has been conclusively demonstrated to reduce the lifetime risk of male acquisition of HIV. The strategy has been adopted as a component of a comprehensive strategy towards achieving an AIDS-free generation. A number of countries in which prevalence of HIV is high and circumcision is low have been identified as a priority, where innovative approaches to scale-up are currently being explored. Rwanda, as one of the priority countries, has faced a number of challenges to successful scale-up. We discuss here how simplifications in the procedure, addressing a lack of healthcare infrastructure and mobilizing resources, and engaging communities of both men and women have permitted Rwanda to move forward with more optimism in its scale-up tactics. Examples from Rwanda are used to highlight how these barriers can and should be addressed.

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 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)0.002
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 score0.368
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.053
GPT teacher head0.337
Teacher spread0.284 · 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 teacher head, 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

Citations7
Published2014
Admission routes1
Has abstractyes

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