Confronting the public health challenge of inaccessible COVID-19 home tests: insights from the RADx® Tech Accessibility Initiative
Bibliographic record
Abstract
COVID-19 home tests first distributed by the U.S. Government in early 2022 proved inaccessible to Americans with no vision, low vision, limited dexterity, and certain aging-related impairments. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) leveraged a multimillion-dollar investment to address this challenge via the Rapid Acceleration of Diagnostics (RADx®) Tech Accessibility Initiative. What follows is a case study report on the March 2022-June 2023 implementation of this initiative, which was later expanded into a larger-scale program called RADx Tech III (September 2022-present). The initiative unveiled a substantial gap in resources guiding accessible product development and applied crisis response funding to bridge this gap. Beyond the primary goal of improving accessibility of COVID-19 home tests, the initiative was successful in developing an expert resource pool, documenting best practices for design of accessible home tests, and generating/validating a framework for future accessibility initiatives.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".