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Record W3044046216 · doi:10.1097/qad.0000000000002605

Preserving 2 decades of healthcare gains for Africa in the coronavirus disease 2019 era

2020· article· en· W3044046216 on OpenAlexaff
Sonak Pastakia, Paula Braitstein, Omar Galárraga, Becky L. Genberg, Jamil Said, Rajesh Vedanthan, Juddy Wachira, Joseph W. Hogan

Bibliographic record

VenueAIDS · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute of Mental HealthNational Heart, Lung, and Blood Institute
KeywordsHealth carePandemicCoronavirus disease 2019 (COVID-19)Social distanceHealthcare systemPublic relationsMedicineBusinessPolitical scienceEconomic growthDiseaseInfectious disease (medical specialty)Economics

Abstract

fetched live from OpenAlex

: As coronavirus disease 2019 (Covid-19) restrictions upend the community bonds that have enabled African communities to thrive in the face of numerous challenges, it is vital that the gains made in community-based healthcare are preserved by adapting our approaches. Instead of reversing the many gains made through locally driven development partnerships with international funding agencies for other viral diseases like HIV, we must use this opportunity to adapt the many lessons learned to address the burden of Covid-19. Programs like the Academic Model Providing Access to Healthcare are currently leveraging widely available technologies in Africa to prevent patients from experiencing significant interruptions in care as the healthcare system adjusts to the challenges presented by Covid-19. These approaches are designed to preserve social contact while incorporating physical distancing. The gains and successes made through approaches like group-based medical care must not only continue but can help expand upon the extraordinary success of programs like President's Emergency Plan for AIDS Relief.

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)0.000
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 score0.071
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.090
GPT teacher head0.375
Teacher spread0.285 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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