Digital Health Paradox: International Policy Perspectives to Address Increased Health Inequalities for People Living With Disabilities
Bibliographic record
Abstract
The COVID-19 pandemic accelerated the uptake of digital health worldwide and highlighted many benefits of these innovations. However, it also stressed the magnitude of inequalities regarding accessing digital health. Using a scoping review, this article explores the potential benefits of digital technologies for the global population, with particular reference to people living with disabilities, using the autism community as a case study. We ultimately explore policies in Sweden, Australia, Canada, Estonia, the United Kingdom, and the United States to learn how policies can lay an inclusive foundation for digital health systems. We conclude that digital health ecosystems should be designed with health equity at the forefront to avoid deepening existing health inequalities. We call for a more sophisticated understanding of digital health literacy to better assess the readiness to adopt digital health innovations. Finally, people living with disabilities should be positioned at the center of digital health policy and innovations to ensure they are not left behind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 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".