Informing the Canadian Public about Health Topics Using Dashboards: Examining the Usability and Identifying Opportunities to Improve Dashboards for Communicating Health Information
Bibliographic record
Abstract
Dashboards and information visualizations are widely used to inform and aid decision-making across many fields, including healthcare. In response to the COVID-19 pandemic, numerous health organizations developed new dashboards to observe, illustrate and communicate data about the virus to the public, resulting in their widespread adoption. As the number of health dashboards continues to grow, usability factors, such as system visibility, efficiency, and consistency, become increasingly important to ensure that users can easily navigate them and extract valuable insights. This research project initially assessed the usability of three tuberculosis (TB) dashboards using established heuristics specific to information visualization. The heuristic evaluation revealed that, while these dashboards displayed appropriate information coding, two out of the three lacked efficiency in content navigation and contained extraneous material. These findings were then used to inform the development of a new prototype dashboard using publicly available datasets for TB. The prototype leveraged the strengths of the existing dashboards and incorporated design elements such as embedded filtering and a consistent colour scheme. These improvements reduced usability concerns and delivered a superior dashboard to facilitate communication and aid decision-making about TB.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".