Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities
Bibliographic record
Abstract
Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.
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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 | 0.339 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.090 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".