Health Systems and Services During COVID-19: Lessons and Evidence From Previous Crises: A Rapid Scoping Review to Inform the United Nations Research Roadmap for the COVID-19 Recovery
Bibliographic record
Abstract
This rapid scoping review has informed the development of the November 2020 United Nations Research Roadmap for the COVID-19 Recovery, by providing a synthesis of available evidence on the impact of pandemics and epidemics on (1) essential services and (2) health systems preparedness and strengthening. Emerging findings point to existing disparities in health systems and services being further exacerbated, with marginalized populations and low- and middle-income countries burdened disproportionately. More broadly, there is a need to further understand short- and long-term impacts of bypassed essential services, quality assurance of services, the role of primary health care in the frontline, and the need for additional mechanisms for effective vaccine messaging and uptake during epidemics. The review also highlights how trust-of institutions, of science, and between communities and health systems-remains central to a successful pandemic response. Finally, previous crises had repeatedly foreshadowed the inability of health systems to handle upcoming pandemics, yet the reactive nature of policies and practices compounded by lack of resources, infrastructure, and political will have resulted in the current failed response to COVID-19. There is therefore an urgent need for investments in implementation science and for strategies to bridge this persistent research-practice gap.
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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 | 0.059 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".