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Record W4386656809 · doi:10.1093/eurpub/ckab164.736

9.F. Workshop: Rapid evidence synthesis to inform the national response to the COVID-19 pandemic in Ireland

2021· article· en· W4386656809 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicStatutory lawPublic healthWork (physics)Scientific evidencePublic relationsEvidence-based practicePolitical scienceCoronavirus disease 2019 (COVID-19)Quality (philosophy)MedicineNursingEngineeringAlternative medicineDiseaseLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.198
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1980.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.223
GPT teacher head0.419
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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