What makes COVID-19 dashboards actionable? Lessons learned from international and country-specific studies of COVID-19 dashboards and with dashboard developers in the WHO European Region
Bibliographic record
Abstract
Abstract Although COVID-19 dashboards may be widely accessible, their effective use to modify the course of the pandemic through the translation of data to information, information to opinions, and opinions to decision-making is determined by their actionability. To be actionable, the information should be both fit for purpose-meeting a specific information need-and fit for use-placing the right information into the right hands at the right time and in a manner that can be understood. Recognizing the sustained importance of COVID-19 dashboards as a tool for pandemic reporting, studies to describe this landscape and take stock of experiences are of critical importance for better preparedness in future public health crises. Since early 2020, the international network of healthcare performance intelligence researchers-HealthPros-in collaboration with partners including the WHO Regional Office for Europe, have worked to systematically study the actionability of COVID-19 dashboards by comparatively studying their composition, changes over time and development process. In this presentation, results from this series of research will be summarized. Topics to be covered include: A summary of seven key features constituting actionable dashboards resulting from a descriptive assessment and scoring of 158 dashboards from more than 53 countries worldwide. Insights into changes to dashboards over the course of 2020-21 from country-specific studies in Canada, the Netherlands and Italy. Lessons learned from the perspective of COVID-19 national, governmental dashboard developers in the WHO European Region, including key enablers and barriers to the development, maintenance and evolution of dashboards over the course of the pandemic.
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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 | 0.192 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".