COVID-19 and beyond: how lessons and evidence from implementation research can benefit health systems’ response and preparedness for COVID-19 and future epidemics
Bibliographic record
Abstract
Early in the COVID-19 pandemic-and based on limited data on the novel coronavirus-it was projected that African countries will be ravaged and the health systems overwhelmed. Fortunately, Africa has so far defied these dire predictions. Many factors account for the less dramatic outcome, in particular the local know-how gained through dealing with previous epidemics, such as Ebola, and the early and coordinated political and public health response, applying a combination of containment and mitigation measures. However, these same measures, exacerbated by existing inequalities, have had negative impacts on vulnerable populations, notably women and children. Furthermore, the observed deterioration of access to and provision of essential health services will likely continue and worsen in countries experiencing future waves of COVID-19 and lacking access to vaccines. The impact of the pandemic on health systems may be one of Africa's main COVID-19 challenges and women and children its greatest victims. In this article, we argue that just as learning from previous epidemics and coordinated preparation informed Africa's response to COVID-19, knowledge, innovations and resources from recent implementation research can be leveraged to mitigate the pandemic's effects and inform recovery efforts. As an example, we present the proven model and multifaceted approach of the Innovating for Maternal and Child Health in Africa Initiative and describe how such a model could be readily applied to building the robust and equitable systems needed to tackle future stresses and shocks, such as epidemics, on health systems while maintaining essential routine services.
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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 | 0.252 | 0.348 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.023 | 0.044 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".