Preserving 2 decades of healthcare gains for Africa in the coronavirus disease 2019 era
Bibliographic record
Abstract
: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".