Digital Health for Vulnerable Populations: From Co-design to Scaling and Replication
Bibliographic record
Abstract
Background The COVID-19 pandemic has made it clear that technology access, digital literacy, and telehealth access have become more crucial than ever before. At the Center for Information Technology Research in the Interest of Society (CITRIS) at the University of California, 2 projects are focused on communities have the least access to quality health care services, including low-income workers in rural areas as well as low-income older adults in their community. Objective Co-designed technology innovation is a core competency of CITRIS Health. This presentation will focus on 2 of CITRIS Health’s co-designed signature programs: ACTIVATE and Lighthouse. Co-designed innovations have the intended outcomes of improving access to technology, increasing technology literacy, and ultimately improving health outcomes. Methods Co-design refers to a participatory approach to designing solutions, in which community members are treated as equal collaborators in the design process—they give feedback, and they try out devices. It is part of an innovation process. Key components of a co-design process involve the following: intentionally involving users in designing solutions, postponing design decisions until after gathering feedback, synthesizing feedback from participants into insights, and developing solutions based on feedback. Results Both projects have undergone formal evaluations to assess the process of implementation as well as outcomes. Additionally, each project has a systematic process for monitoring its own implementation and key metrics. Common near-term outcomes include positive feedback from co-designers about the inclusivity of the design progress and optimism that technology selections, training, and interventions will lead to the intended outcomes. Conclusions Ultimately, the intention of these co-designed innovations is to create models that are feasible and sustainable. They will provide a roadmap for both public and private partners, setting a gold standard in California and across the nation.
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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 | 0.302 | 0.378 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".