Challenges to evidence-informed decision-making in the context of pandemics: qualitative study of COVID-19 policy advisor perspectives
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The exceptional production of research evidence during the COVID-19 pandemic required deployment of scientists to act in advisory roles to aid policy-makers in making evidence-informed decisions. The unprecedented breadth, scale and duration of the pandemic provides an opportunity to understand how science advisors experience and mitigate challenges associated with insufficient, evolving and/or conflicting evidence to inform public health decision-making. OBJECTIVES: To explore critically the challenges for advising evidence-informed decision-making (EIDM) in pandemic contexts, particularly around non-pharmaceutical control measures, from the perspective of experts advising policy-makers during COVID-19 globally. METHODS: We conducted in-depth qualitative interviews with 27 scientific experts and advisors who are/were engaged in COVID-19 EIDM representing four WHO regions and 11 countries (Australia, Canada, Colombia, Denmark, Ghana, Hong Kong, Nigeria, Sweden, Uganda, UK, USA) from December 2020 to May 2021. Participants informed decision-making at various and multiple levels of governance, including local/city (n=3), state/provincial (n=8), federal or national (n=20), regional or international (n=3) and university-level advising (n=3). Following each interview, we conducted member checks with participants and thematically analysed interview data using NVivo for Mac software. RESULTS: Findings from this study indicate multiple overarching challenges to pandemic EIDM specific to interpretation and translation of evidence, including the speed and influx of new, evolving, and conflicting evidence; concerns about scientific integrity and misinterpretation of evidence; the limited capacity to assess and produce evidence, and adapting evidence from other contexts; multiple forms of evidence and perspectives needed for EIDM; the need to make decisions quickly and under conditions of uncertainty; and a lack of transparency in how decisions are made and applied. CONCLUSIONS: Findings suggest the urgent need for global EIDM guidance that countries can adapt for in-country decisions as well as coordinated global response to future pandemics.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it