Editorial: Socioeconomic inequalities in digital health
Bibliographic record
Abstract
ensure progress benefits all. As digital technologies increasingly shape the contours of healthcare, this principle demands renewed reflection. In a globally connected world, pandemics and health crises remind us that no population is truly isolated. Inequities in digital health preparedness and response in one region can have ripple effects worldwide.Thus, global cooperation and equitable digital infrastructure are essential.The research contributions in this special collection, Socioeconomic Inequalities in Digital Health, illuminate both the opportunities and the tensions at the heart of this issue. A narrative review by Awosiku et al. (2025) Crucially, digital exclusion is not confined to low-income countries. A cross-sectional study by Claudio et al. (2025) at a paediatric hospital in Canada reveals that even in high-income settings, the digital divide persists. While most caregivers had access to digital devices, 23% encountered challenges with technological familiarity and expressed concerns about data security. These findings highlight that digital readiness cannot be assumed and must be intentionally cultivated, especially among vulnerable and underserved groups. Together, these studies present a complex but hopeful picture. They remind us that digital health, if designed inclusively, can be a powerful tool for equity, but if left unchecked, it may reproduce the very injustices it seeks to overcome. These insights challenge us to see digital health not merely as a technical endeavour but as a profoundly human one. As The Promise of World Peace observes, "The process of planetization is at work, integrating peoples and cultures, reshaping institutions, and forging bonds of mutual dependence and collaboration." When guided by compassion and foresight, digital health can become a vital part of this process, promoting dignity, justice, and well-being across all levels of society.In the context of digital health, "planetization" means designing systems that transcend national, racial, and economic divides-systems grounded in ethics and shaped by the lived realities of those they aim to serve. It reminds us that innovation without purpose is insufficient. Only through equity, accessibility, and cultural humility can digital health fulfill its transformative potential. This means not only addressing disparities but reimagining health systems to reflect the unity of humanity.To realise this vision, researchers must continue to interrogate both the impacts and blind spots of digital innovation; policymakers must craft frameworks that prioritise inclusion over scale; and practitioners must advocate for solutions that are as compassionate as they are cutting-edge. The task before us is not only technical-it is moral, social, and shared.Inclusive digital health policies must prioritise the marginalised, not merely enhance convenience for the already privileged. By prioritising the needs of the most vulnerable, we strengthen the fabric of health systems for all. When guided by shared purpose, justice, and the recognition of our oneness, digital health can unite humanity, fostering solidarity instead of division. This Research Topic offers both evidence and inspiration to help guide us along that vital path.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".