Usability and usefulness of U. S. federal and state public health data dashboards: implications for improving data access and use
Bibliographic record
Abstract
Introduction: Dashboards that afford timely access to credible, relevant, and actionable data can significantly improve public health decision-making at all levels. As dashboards becomes more ubiquitous, it is imperative to proactively consider how they may be optimally designed to be usable and useful to users. Methods: = 210) was utilized to describe and compare common design elements and data characteristics of dashboards. A standardized valid and reliable instrument was used to extract data for assessing dashboards' usability and usefulness. Results: Dashboards are primarily designed for epidemiological surveillance and assessing disparities. Both federal and state dashboards rely heavily on data collected by federal agencies but many state dashboards also draw on local data. Vulnerable subpopulations are underrepresented in data used in dashboards. Federal dashboards score higher than state dashboards on usability but are comparable in usefulness. About one-third of state dashboards are hosted on third-party platforms and are prone to access disruptions. Conclusion: Usability and usefulness of public health dashboards can be significantly enhanced by streamlining and enhancing users' experience and incorporating additional customization and analytical affordances. A uniform set of best-practices and standards for optimizing dashboard design and implementation does not yet exist as research on this topic is lagging. Policy implications: Additional federal and state investments are needed to build and maintain a robust infrastructure for developing, improving, and sustaining public health dashboards and incentivize rigorous, theory grounded research to optimize usability and usefulness of dashboards.
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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 | 0.086 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".