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Record W2075537821 · doi:10.1097/acm.0b013e318160b5cf

The Academic Alliance for AIDS Care and Prevention in Africa

2008· article· en· W2075537821 on OpenAlexaff
Merle A. Sande, Allan Ronald

Bibliographic record

VenueAcademic Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Manitoba
FundersPfizer
KeywordsAllianceHealth careScholarshipMedicineFamily medicineNursingMedical educationPolitical science

Abstract

fetched live from OpenAlex

Fourteen university-based Ugandan and North American physicians in 2001 founded a unique organization at Makerere University Faculty of Medicine in Uganda, the Academic Alliance for AIDS Care and Prevention in Africa (AA), with programs in training, research, prevention, and care. Funding was obtained from Pfizer, Inc.; in 2004, the Infectious Disease Institute (IDI) was built to house the flagship training and care programs of the AA. Although HIV/AIDS was the initial priority, other infectious diseases have been added to the AA's mission, and training has been provided to date to individuals from 26 countries in Africa. These programs are now supported by the Academic Alliance Foundation (AAF), which is based in the United States. The authors describe the AA's programs to train health care workers and to offer ongoing support for health care professionals throughout Africa, as well as efforts to strengthen the health care system within Uganda. They also outline research and clinical services carried out by the IDI and research scholarship programs supported by the AAF. They state that it is too early to judge the success of the AA, and they acknowledge that the lack of trained health care providers and of an adequate care infrastructure are major challenges in Africa. They conclude that the critical challenge facing the AAF and the IDI is to diversify the funding base to sustain current program levels. They then enumerate issues that must be addressed to ensure long-term organizational strength and stability.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.067
GPT teacher head0.369
Teacher spread0.302 · 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 designNot applicable
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

Citations23
Published2008
Admission routes1
Has abstractyes

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