Health Literacy for the General Public: Making a Case for Non-Trivial Visualizations
Bibliographic record
Abstract
Health literacy is concerned with the degree to which individuals can access and understand information to make health decisions. The multifaceted nature of health data presents challenges for individuals seeking to improve their understanding of health. To aid health literacy efforts, we have developed HealthConfection, a visualization tool that uses elaborate and non-typical interactive visualizations to represent health data. In this paper, we report on two studies we conducted with HealthConfection. In the first study, we investigate whether individuals can learn to use non-typical visualizations, and the impact that short, minimalist video tutorials will have on participants’ understanding of the visualizations. The findings from this study suggest that individuals can learn to use non-typical visualizations and that participants who used the tutorials achieved higher scores than those without tutorials. This work indicates that non-typical visualizations are a viable option for conveying complex datasets. Based on this foundation, we conducted a second study to investigate if non-typical visualizations can improve health literacy for the general public. Results show that participants who used HealthConfection achieved higher scores than those who did not interact with the tool. Our work suggests that non-typical visualizations can be used to improve health literacy.
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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 | 0.034 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| 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".