Soft power and global health: the sustainable development goals (SDGs) era health agendas of the G7, G20 and BRICS
Bibliographic record
Abstract
BACKGROUND: In 2017, the G20 health ministers convened for the first time to discuss global health and issued a communiqué outlining their health priorities, as the BRICS and G7 have done for years. As these political clubs hold considerable political and economic influence, their respective global health agendas may influence both global health priorities and the priorities of other countries and actors. METHODS: Given the rising salience of global health in global summitry, we analyzed the health ministerial communiqués issued by the BRICS, G7 and G20 after the SDGs were adopted in 2015. We compared the stated health priorities of the BRICS, G7 and G20 against one another and against the targets of SDG 3 on health, using a traffic light system to assess the quality of their commitments. RESULTS: With regard to the SDG 3 targets, the BRICS, G7 and G20 priorities overlapped in their focus on emergency preparedness and universal health coverage, but diverged in areas of environmental pollution, mental health, and maternal and child health. Health issues with considerable associated burdens of disease, including substance use, road traffic injuries and sexual health, were missing from the agendas of all three political clubs. In terms of SDG 3 principles and ways of working, the BRICS, G7 and G20 varied in their emphasis on human rights, equity and engagement with non-state actors, but all expressed their explicit commitment to Agenda 2030. CONCLUSIONS: The leadership of BRICS, G7 and G20 on global health is welcome. However, their relatively narrow focus on the potential impact of ill-health primarily in relation to the economy and trade may not be sufficiently comprehensive to achieve the Agenda 2030 vision of promoting health equity and leaving no-one behind. Recommendations for the BRICS, G7 and G20 based on this analysis include: 1) expanding focus to the neglected SDG 3 health targets; 2) placing greater emphasis on upstream determinants of health; 3) greater commitment to equity and leaving no-one behind; 4) adopting explicit commitments to rights-based approaches; and 5) making commitments that are of higher quality and which include time-bound quantitative targets and clear accountability mechanisms.
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 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".