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An appeal for large scale production of antiretroviral drugs in Africa

2016· article· en· W2521193918 on OpenAlexaff
Nkamedjie Pete Patrick Martial, Isidore Sieleunou

Bibliographic record

VenuePan African Medical Journal · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsMedicineEconomic growthGovernment (linguistics)PandemicSustainabilityAntiretroviral treatmentPoliticsMillennium Development GoalsScale (ratio)Development economicsPublic healthDeveloping countryDistribution (mathematics)Human immunodeficiency virus (HIV)ScarcityAntiretroviral therapyCoronavirus disease 2019 (COVID-19)EconomicsPolitical scienceDiseaseImmunologyNursingViral loadMarket economy

Abstract

fetched live from OpenAlex

The Acquired Immuno Deficiency Syndrome (AIDS) remains a major global public health challenge especially in Africa. The deadline set for the Millennium Development Goals (MDGs) has elapsed, meanwhile most low and middle income countries did not reach the targets. With regards to the fight against HIV / AIDS, many African countries show slow progress in implementing efficient and effective strategies to counter this pandemic. The fact that most HIV/AIDS programs in Sub-Saharan African countries are still very dependent on external funding to carry out their activities, including the supply of Antiretroviral Treatment (ART), highlights the concern of sustainability. So far, solutions that have been proposed are mainly symptomatic, claiming more budget commitment from government. Without rejecting this view, we call for the implementation of sustainable solutions to deal with the long term ART challenges. A way forward is to promote the establishment of an effective machinery for the manufacturing and large scale distribution of ART. In addition to the health gains, we argue that such an initiative would have a three-dimensional impact: (i) political, (ii) economic and (iii) social.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.289
Teacher spread0.255 · 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 designTheoretical or conceptual
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

Citations8
Published2016
Admission routes1
Has abstractyes

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