Advancing Knowledge Translations to Contribute to Resilience in Emergency Preparedness, Response and Recovery: The Role of Evidence Aid
Bibliographic record
Abstract
Introduction: Policymakers, practitioners and the public all have a role in health emergency and disaster risk management (Health EDRM). They need to access, understand and use evidence from research to take actions to reduce health risks and harm. They need the best available evidence to maximize their ability to save lives and reduce suffering. Evidence Aid seeks to meet this need through collections of specially prepared plain-language summaries of systematic reviews, freely available online in multiple languages (www.EvidenceAid.org). The summaries and webpages can be linked to reference management software and embedded in other websites. Method: Evidence Aid has added a substantial number of summaries to its collections since 2020, for example, adding a collection for reviews of relevance to the COVID-19 pandemic and its associated measures. From 2021, Evidence Aid built on its partnership with the Pan American Health Organization (PAHO/WHO) to identify and summarize reviews relevant to building resilience into health systems. This included enhancements enriching the content of each summary with the authors’ implications for practice and research, equity considerations and funding sources. Results: In November 2022, the Resilient Health Systems collection contained more than 200 summaries relevant to ensuring that health systems are resilient to emergencies, disasters and related challenges. There were also 600 summaries relevant to the COVID-19 pandemic, 150 on the health of refugees and asylum seekers, more than 100 on physical and mental health impacts of disasters and 110 on preventing and treating acute malnutrition. Conclusion: Evidence Aid’s 1000+ summaries of systematic reviews relevant to Health EDRM provide a unique gateway into this evidence base for policymakers, practitioners and the public wishing to ensure that disaster preparedness, response, recovery and rehabilitation are effective and efficient. It should be a key component in helping people and organizations to care, cope and overcome in an increasingly challenging world.
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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 | 0.351 | 0.642 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.047 | 0.034 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".