Digital determinants of health: Editorial
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 systems have grown very rapidly in the last decade in high-income regions, including North America and Europe, and also in many low-and middle-income regions While core health information systems like electronic health records and radiology information systems are central to this progress, equally important are mobile health tools, telehealth systems, and certain health uses of social media. The benefits shown in healthcare delivery and public health from the use of these systems are predicated both on access to this diverse set of tools (based on access to technology, power, networking, and training) and the ability of healthcare workers and patients to use them effectively (digital literacy). In this edition of the journal, the focus is on a new concept: Digital Determinants of Health (DDoH), described below. Clearly, there are inequalities of access to healthcare and health technologies around the world and many different ways of delivering care. The concern here is with inequities in access to care driven by poverty, mismanagement, and systems that continue to be designed without a core focus on the right of all patients from all groups and abilities and in all locations to good quality health and healthcare. Health equity is "the absence of health inequities, differences in health that are unnecessary, avoidable, unfair, and unjust" Using a range of methods, the authors have explored the importance of different aspects of this emerging area of research, including surveying the literature in 3 scoping reviews, and developed new frameworks for describing and analyzing the importance of the different factors and potential biases. The comprehensiveness of this work has been aided by a policy on inclusion of authors, reviewers, and editors from different communities and countries at PLOS Digital Health.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it