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Record W2137903189 · doi:10.1002/sim.6572

The Africa Center for Biostatistical Excellence: a proposal for enhancing biostatistics capacity for sub‐Saharan Africa

2015· article· en· W2137903189 on OpenAlexaff
Rhoderick Machekano, Taryn Young, Simbarashe Rusakaniko, Patrick Musonda, Ben Sartorius, Jim Todd, Greg Fegan, Lehana Thabane, Usuf Chikte

Bibliographic record

VenueStatistics in Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcMaster University
FundersFogarty International CenterNational Institutes of HealthU.S. Public Health ServiceWellcome TrustU.S. President’s Emergency Plan for AIDS Relief
KeywordsBiostatisticsBrainstormingCenter of excellenceEconomic shortageExcellenceCapacity buildingMedical educationCapacity developmentStatisticianLibrary sciencePolitical scienceMedicineComputer scienceGeographyMathematicsStatisticsEnvironmental planningPublic healthArtificial intelligenceNursing

Abstract

fetched live from OpenAlex

Sub-Saharan Africa has a shortage of well-trained biomedical research methodologists, in particular, biostatisticians. In July 2014, a group of biostatisticians and researchers from the region attended a brainstorming workshop to identify ways in which to reduce the deficit in this critical skill. The workshop recognized that recommendations from previous workshops on building biostatistics capacity in sub-Saharan Africa had not been implemented. The discussions culminated with a proposal to setup an Africa Center for Biostatistical Excellence, a collaborative effort across academic and researcher institutions within the region, as a vehicle for promoting biostatistics capacity building through specialized academic masters programs as well as regular workshops targeting researchers.

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)0.006
metaresearch head score (Gemma)0.241
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.235
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.241
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.175
GPT teacher head0.436
Teacher spread0.261 · 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.

Study designNot applicable
Domainnot available
GenreMethods

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

Citations20
Published2015
Admission routes1
Has abstractyes

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