Digital health and equitable access to care
Bibliographic record
Abstract
Research on digital health equity has developed in important ways especially since the onset of the COVID-19 pandemic, with a series of clear recommendations now established for policy and practice. However, research and policy addressing the health system dimensions of digital health equity is needed to examine the appropriate roles of digital technologies in enabling access to care. We use a highly cited framework by Levesque et al on patient-centered access to care and the World Health Organization's framework on digitally enabled health systems to generate insights into the ways that digital solutions can support access to needed health care for structurally marginalized communities. Specifically, we mapped the frameworks to identify where applications of digital health do and do not support access to care, documenting which dimensions of access are under-addressed by digital health. Our analysis suggests that digital health has disproportionately focused on downstream enablers of access to care, which are low-yield when equity is the goal. We identify important opportunities for policy makers, funders and other stakeholders to attend more to digital solutions that support upstream enablement of peoples' abilities to understand, perceive, and seek out care. These areas are an important focal point for digital interventions and have the potential to be more equity-enhancing than downstream interventions at the time that care is accessed. Overall, we highlight the importance of taking a health system perspective when considering the roles of digital technologies in enhancing or inhibiting equitable access to needed health care.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| 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".