Exploring the Equity Impact of Current Digital Health Design Practices: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The field of digital health has grown rapidly in part due to digital health tools' potential to reduce health inequities. However, such potential has not always been realized. The design approaches used in digital health are one of the known aspects that have an impact on health equity. OBJECTIVE: The aim of our scoping review will be to understand how design approaches in digital health have an impact on health equity. METHODS: A scoping review of studies that describe how design practices for digital health have an impact on health equity will be carried out. The scoping review will follow the methodologies laid out by Arksey and O'Malley, the Joanna Briggs Institute, and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The PubMed, Embase, Web of Science, and ACM Digital Library databases will be searched for peer-reviewed papers. The ProQuest Dissertations and Theses and Global Index Medicus databases will be searched for gray literature. The results will be screened against our inclusion and exclusion criteria. Subsequently, the data extracted from the included studies will be analyzed. RESULTS: As of March 2022, a preliminary search of the peer-reviewed databases has yielded over 4900 studies, and more are anticipated when gray literature databases are searched. We expect that after duplicates are removed and screening is completed, a much smaller number of studies will meet all of our inclusion criteria. CONCLUSIONS: Although there has been much discussion about the importance of design for lowering barriers to digital health participation, the evidence base demonstrating its impacts on health equity is less obvious. We hope that our findings will contribute to a better understanding of the impact that design in digital health has on health equity and that these findings will translate into action that leads to stronger, more equitable health care systems.
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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 | 0.140 | 0.135 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.097 | 0.023 |
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".