Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital health technologies hold promises for reducing health care costs, enhancing access to care, and addressing labor shortages. However, they risk exacerbating inequalities by disproportionately benefitting a subset of the population. Use of digital technologies accelerated during the Covid-19 pandemic. Our scoping review aimed to describe how inequalities related to their use were conceptually assessed during and after the pandemic and understand how digital strategies and policies might support digital equity. We used the PRISMA Extension for scoping reviews, identifying 2055 papers through an initial search of 3 databases in 2021 and complementary search in 2022, of which 41 were retained. Analysis was guided by the eHealth equity framework. Results showed that digital inequalities were reported in the U.S. and other high-income countries and were mainly assessed through differences in access and use according to individual sociodemographic characteristics. Health disparities related to technology use and the interaction between context and technology implementation were more rarely documented. Policy recommendations stressed the adoption of an equity lens in strategy development and multilayered and intersectoral collaboration to align interventions with the needs of specific subgroups. Finally, findings suggested that evaluations of health and wellbeing distribution related to the use of digital technologies should inform digital strategies and health policies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it