A Practical Guide to Participatory Design Sessions for the Development of Information Visualizations: Tutorial
Bibliographic record
Abstract
Unlabelled: Participatory design is an increasingly common informatics method to engage intended audiences in the development of health-related resources. Participatory design is particularly helpful for developing information visualizations that aim to improve health outcomes by means of improved comprehension, communication or engagement, and subsequent behavior changes. Existing literature on participatory design lacks the practical details that influence the success of the method and does not address emergent issues, such as strategies to enhance internet-based data collection. In this tutorial, our objective is to provide practical guidance on how to prepare for, conduct, and analyze participatory design sessions for information visualization. The primary audience for this tutorial is research teams, but this guide is relevant for organizations and other health professionals looking to design visualizations for their patient populations, as they can use this guide as a procedural manual. This start-to-finish guide provides information on how to prepare for design sessions by setting objectives and applying theoretical foundations, planning design sessions to match project goals, conducting design sessions in different formats with varying populations, and carrying out effective analysis. We also address how the methods in this guide can be implemented in the context of resource constraints. This tutorial contains a glossary of relevant terms, pros and cons of variations in the type of design session, an informed consent template, a preparation checklist, a sample design session guide and selection of useful design session prompts, and examples of how surveys can supplement the design process.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.123 | 0.062 |
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".