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Record W1997465156 · doi:10.1177/154510970200100202

Justice and HIV Care in Africa—Antiretrovirals in Perspective

2002· review· en· W1997465156 on OpenAlexaff
Stan Houston

Bibliographic record

VenueJournal of the International Association of Physicians in AIDS Care · 2002
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineHealth careInjusticeEquity (law)Economic growthPublic healthEnvironmental healthPublic relationsPolitical scienceNursingEconomics

Abstract

fetched live from OpenAlex

The immense burden of HIV disease in sub-Saharan Africa has focused international interest on HIV care, especially on the lack of access to antiretroviral therapy (ART). Difficulties in implementing ART in Africa include drug costs, adequate long-term funding sources, assurance of drug quality, and rapid development of the human resources and healthcare infrastructure needed to deliver ART. Important questions requiring study are the minimum level of laboratory monitoring and clinical support consistent with good treatment outcomes, the impact of antiretroviral drug resistance on treated individuals and communities, and the effect of ART on transmission at a community level. There are some concerns and risks. First, a focus on treatment could compromise the commitment of individuals to risk-reduction, and of governments to prevention. Second, health equity could be reduced, by diverting scarce public funds from basic care for the poorest, to costly disease-suppressive care for a small and probably elite group. In conclusion, while prevention must be the first priority, care is also essential. The vast prevailing economic inequity between the world's rich and poor is the fundamental determinant of inequities in health and healthcare, including care for HIV. The global community of healthcare workers must focus its substantial influence on changing political and economic policies that foster injustice and AIDS.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.375
Teacher spread0.340 · 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 designOther design
Domainnot available
GenreReview

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

Citations9
Published2002
Admission routes1
Has abstractyes

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