Business as usual? The role of BRICS co- operation in addressing health system priorities in East and Southern Africa
Bibliographic record
Abstract
There has been increased interest in whether “South-‐South” co-‐operation by Brazil, Russia, India, China and South Africa (BRICS) advances more equitable initiatives for global health. This article examines the extent to which resolutions, commitments, agreements and strategies from BRICS and Brazil, India and China (BIC) address regionally articulated policy concerns for health systems in East and Southern Africa (ESA) within areas of resource mobilization, research and development and local production of medicines, and training and retention of health workers. The study reviewed published literature and implemented a content analysis on these areas in official BRICS and ESA regional policy documents between 2007 and 2014. The study found encouraging signals of shared policy values and mutuality of interest, especially on medicines access, although with less evidence of operational commitments and potential divergence of interest on how to achieve shared goals. The findings indicate that African interests on health systems are being integrated into south-‐south BRICS and BIC platforms. It also signals, however, that ESA countries need to proactively ensure that these partnerships are true to normative aims of mutual benefit, operationalize investments and programs to translate policy commitments into practice and strengthen accountability around their implementation.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".