COVID-19-related global health governance and population health priorities for health equity in G20 countries: a scoping review
Bibliographic record
Abstract
Since the declaration of the COVID-19 pandemic, the promotion of health equity including the health of various population sub-groups has been compromised, human rights jeopardised, and social inequities further exacerbated. Citizens worldwide, including in the Group of 20 (G20) countries, were affected by both global health governance (GHG) processes and decisions and public health measures taken by governments to respond to COVID-19. While it is critical to swiftly respond to COVID-19, little is known about how and to what extent the GHG is affecting population health priorities for health equity in global economies such as the G20 countries. This scoping review synthesised and identified knowledge gaps on how the COVID-19-related GHG is affecting population health priorities for policy, programme, and research in G20 countries. We followed the five-stage scoping review methodology promoted by Arksey and O'Malley and the PRISMA Extension for Scoping Reviews guidelines. We searched four bibliographic databases for references conducted in G20 countries and regions and published in English and French, between January 2020 and April 2023. Out of 4,625 references and after two phases of screening, 14 studies met the inclusion criteria. G20 countries included in the review were Australia, Brazil, Canada, China, France, India, Italy, Japan, Russia, South Africa, the United Kingdom, the United States of America, and the European Union. We found insufficient collaboration and coordination and misalignment among governance actors at multiple levels. In most cases, equity considerations were not prioritised while unequal consequences of COVID-19 public health measures on population groups were widely reported. COVID-19-related population health priorities mainly focused on upstream and midstream determinants of health. Our scoping review showed the stark inequities of COVID-19 public health outcomes, coupled with a prevalent lack of coherent collaboration and coordination among governance actors. Moreover, governance as an object of empirical study is still emerging when examining its intersection with global health and population health policy, programme, and research. An urgent shift is required to effectively act upon structural health determinants that include transformative and comprehensive policies for prevention, equity, resilience, and sustainable health.
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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 | 0.032 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".