9.F. Workshop: Rapid evidence synthesis to inform the national response to the COVID-19 pandemic in Ireland
Bibliographic record
Abstract
Abstract The Health Information and Quality Authority (HIQA) is an independent statutory authority in Ireland. Since March 2020, HIQA has been conducting evidence syntheses to support decision-making by the Irish National Public Health Emergency Team (NPHET). This work has informed guidance developed by national and international agencies such as the European Centre for Disease Prevention and Control (ECDC), Newfoundland and Labrador Center for Applied Health Research, Sciensano (Belgium), Alberta Health Services and Sante Publique France. The HIQA COVID-19 Evidence Synthesis Team draws on a broad range of evidence with expert clinical and public health input from HIQA's COVID-19 Expert Advisory Group, and produces a range of outputs to best suit the evidence needs of decision-makers. Across the world, agencies have had to adapt rapidly to provide up-to-date COVID-19 research evidence to decision-makers. Similarly, evidence users, including the public, have had to assimilate information on COVID-19 generated by such agencies. Significant challenges exist given the speed at which the evidence base is evolving on COVID-19, its sometimes conflicting nature and often poor quality, and the subsequent influence of its dissemination on policy and on individual behaviour and risk perceptions. This workshop describes the work of HIQA's COVID-19 Evidence Synthesis Team in its role to support the national response to the COVID-19 pandemic. The aim of this workshop is to share learnings on the structure, methodological approaches, and impact of a COVID-19 Evidence Synthesis Team. The presenters who all work on, or are affiliated with HIQA's COVID-19 Evidence Synthesis Team, will discuss a range of topics including the establishment of the team, the conduct of rapid evidence syntheses, and the impact and media coverage of the outputs. The presenters will each present their topic in turn and will allow ample time for engagement, and will use e-voting systems, to enable shared learning. The objectives for this workshop are to: Describe the establishment and organisation of a COVID-19 Evidence Synthesis Team Provide case examples of completed rapid evidence syntheses Examine the media coverage of selected outputs Present the findings of a an in-action evaluation Share learnings on the organisation of evidence synthesis teams, the conduct of rapid evidence syntheses and the subsequent communication of findings. As the pandemic moves into a new phase it is important that agencies involved in conducting, and using the findings of, evidence syntheses reflect on processes and impact to-date. Lessons learned from the COVID-19 evidence synthesis experience can inform the development of future evidence synthesis approaches as well as support future pandemic preparedness. There is a need to remain flexible and to innovate, and this workshop presents an ideal opportunity for a European public health audience to share experiences and ideas. Key messages The challenges and opportunities associated with conducting rapid evidence synthesis during a pandemic will be explored. This workshop will provide a space to share learnings and ideas.
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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 | 0.237 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".