Digital Health Intervention Design and Deployment for Engaging Demographic Groups Likely to Be Affected by the Digital Divide: Protocol for a Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital health interventions refer to interventions designed to support health-related knowledge transfer and are delivered via digital technologies, such as mobile apps. Digital health interventions are a double-edged sword: they have the potential to reduce health inequalities, for example, by making treatments available remotely to rural populations underserved by health care facilities or by helping to overcome language barriers via in-app translation services; however, if not designed and deployed with care, digital health interventions also have the potential to increase health inequalities and exacerbate the effects of the digital divide. OBJECTIVE: The aim of this study is to review ways to mitigate the digital divide through digital health intervention design, deployment, and engagement mechanisms sensitive to the needs of digitally excluded populations. METHODS: This protocol outlines the procedure for a systematic scoping review that follows the methodology recommended by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidance. The following databases will be searched for primary research studies published in English from October 1, 2011, to October 1, 2021: Cochrane Library, Epistemonikos, NICE Evidence, PROSPERO, PubMed (with MEDLINE and Europe PMC), and Trip. In addition, the following sources of gray literature will be searched: Conference Proceedings Citation Index, Health Management Information Consortium, International HTA Database, OpenGrey, The Grey Literature Report, Google Scholar Basic Search UK, MedNar Deep Web Search Engine, and Carrot2. We will select publications that meet the following inclusion criteria: primary research papers that evaluated digital health interventions that describe features of digital health intervention design and deployment that enable or hinder access to and engagement with digital health interventions by adults from demographic groups likely to be affected by the digital divide (eg, older age, minority ethnic groups, lower income, and lower education level). A random selection of 25 publications identified from the search will be double screened by four reviewers. If there is >75% agreement for included/excluded publications, the team will continue to screen all the identified publications. For all included publications, study characteristics will be extracted by one author and checked for agreement by a second author, with any disagreements resolved by consensus among the study team. Consultation digital health intervention design and deployment, and digital health intervention users will also be conducted in parallel. RESULTS: The review is underway and is anticipated to be completed by September 2022. CONCLUSIONS: The results will have implications for researchers and policy makers using digital health interventions for health improvement peripandemic and post pandemic, and will inform best practices in the design and delivery of digital health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/32538.
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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 | 0.132 | 0.119 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.087 | 0.020 |
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".