Challenges to evidence-informed decision-making in the context of pandemics: qualitative study of COVID-19 policy advisor perspectives
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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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 | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".