The Intersections of COVID-19 Global Health Governance and Population Health Priorities: Equity-Related Lessons Learned From Canada and Selected G20 Countries
Bibliographic record
Abstract
Background: COVID-19-related global health governance (GHG) processes and public health measures taken influenced population health priorities worldwide. We investigated the intersection between COVID-19-related GHG and how it redefined population health priorities in Canada and other G20 countries. We analysed a Canada-related multilevel qualitative study and a scoping review of selected G20 countries. Findings show the importance of linking equity considerations to funding and accountability when responding to COVID-19. Nationalism and limited coordination among governance actors contributed to fragmented COVID-19 public health responses. COVID-19-related consequences were not systematically negative, but when they were, they affected more population groups living and working in conditions of vulnerability and marginalisation. Policy options and recommendations: Six policy options are proposed addressing upstream determinants of health, such as providing sufficient funding for equitable and accountable global and public health outcomes and implementing gender-focused policies to reduce COVID-19 response-related inequities and negative consequences downstream. Specific programmatic (e.g., assessing the needs of the community early) and research recommendations are also suggested to redress identified gaps. Conclusion: Despite the consequences of the COVID-19 pandemic, programmatic and research opportunities along with concrete policy options must be mobilised and implemented without further delay. We collectively share the duty to act upon global health justice.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".