Towards Developing an eHealth Equity Conceptual Framework
Bibliographic record
Abstract
Early implementation of electronic health records and patient portals had great promise of addressing the widening disparities in health. However, recent research has found that not only are these disparities persisting, but the differences in health outcomes between populations are increasing. Addressing this gap specific to ehealth calls for attention to health equity. Health equity approaches reveal the systematic and societal structures that contribute to preventable and unjust outcomes for different populations. To conceptualize and apply a health equity approach within ehealth, we propose the eHealth Equity Framework (eHEF). Derived from the World Health Organization's conceptual framework for actions on the social determinants of health, eHEF can be useful for public health practitioners, researchers, policymakers and information technology designers to keep health equity agenda at the forefront of all stages of health information technology lifecycle.
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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 | 0.059 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".