Inequity in Access to and Use of Digital Health Technologies in Routine Heart Failure Care: Protocol for a Scoping Review
Bibliographic record
Abstract
Background: Heart failure (HF) is a global health challenge characterized by high mortality, morbidity, and economic burden. The development of digital health technologies offers promising tools for prevention, early detection, and management of HF, potentially improving prognoses and reducing costs. However, these innovations may also widen existing health disparities related to socioeconomic status, geography, and race/ethnicity. Objective: This scoping review will examine and map existing evidence on socioeconomic, geographic, and racial/ethnic differences in access to and use of digital health technologies for HF care in routine practice. Methods: The writing of this protocol followed the methodological framework by Arksey and O'Malley, including (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies to be included in the review; (4) charting the data; and (5) collating, summarizing, and reporting the results. Eligible studies must examine digital health technologies in adults (aged ≥18 years) with any type of HF and report on social determinants of health, geography, or race/ethnicity. Observational study designs will be included. Searches will be conducted in Embase, PubMed, Google Scholar, and Scopus. A 2-stage screening process will determine study eligibility, and data will be extracted using a standardized form. Results: The project is funded. Data collection is expected to begin by the beginning of 2026. Conclusions: This scoping review will map existing evidence on differences in access to and use of digital health technologies for HF care. The findings are anticipated to highlight patterns and gaps in the literature, informing future research and strategies for equitable implementation.
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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 | 0.115 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.093 | 0.015 |
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".