A systematic approach to equity assessment for digital health interventions: case example of mobile personal health records
Bibliographic record
Abstract
Despite the increasing number of digital health interventions in low- and middle-income countries and other low-resource settings, little attention has been paid to systematically evaluating impacts of these interventions on health equity. In this article, we present a systematic approach for assessing equity impacts of digital health interventions modeled after the Health Equity Impact Assessment of the Ontario Ministry of Health and Long-Term Care. The assessment approach has 4 steps that address (1) scope, (2) potential equity impacts, (3) mitigation, (4) monitoring, and (5) dissemination strategies. The approach examines impacts on vulnerable and marginalized populations and considers various social determinants of health. Equity principles outlined by Whitehead and Dahlgren are used to ensure systematic considerations of all potential equity impacts. The digital health evaluation approach that is presented is applied to a case example of mobile personal health record application in Kenya.
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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 | 0.106 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".