Discourse with Visual Health Data: Design of Human-Data Interaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous work has suggested that large repositories of data can revolutionize healthcare activities; however, there remains a disconnection between data collection and its effective usage. The way in which users interact with data strongly impacts their ability to not only complete tasks but also capitalize on the purported benefits of such data. Interactive visualizations can provide a means by which many data-driven tasks can be performed. Recent surveys, however, suggest that many visualizations mostly enable users to perform simple manipulations, thus limiting their ability to complete tasks. Researchers have called for tools that allow for richer discourse with data. Nonetheless, systematic design of human-data interaction for visualization tools is a non-trivial task. It requires taking into consideration a myriad of issues. Creation of visualization tools that incorporate rich human-data discourse would benefit from the use of design frameworks. In this paper, we examine and present a design process that is based on a conceptual human-data interaction framework. We discuss and describe the design of interaction for a visualization tool intended for sensemaking of public health data. We demonstrate the utility of systematic interaction design in two ways. First, we use scenarios to highlight how our design approach supports a rich and meaningful discourse with data. Second, we present results from a study that details how users were able to perform various tasks with health data and learn about global health trends.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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