Understanding Digital Health Equity: A Conceptual Analysis
Bibliographic record
Abstract
AIM: The purpose of this concept analysis is to clarify the meaning of digital health equity beyond a simplistic definition, obtaining a richer meaning that can guide the digital healthcare landscape. BACKGROUND: With the growing spread of digital health, digital health equity should be at the center of healthcare. Health outcomes for equity-deserving groups may be compromised without a clear understanding of digital health equity. Although the concept of 'health equity' has been analysed before; no concept analysis has been completed for the concept of 'digital health equity'. DESIGN: Concept analysis using Walker and Avant's method. DATA SOURCES: Articles from PubMed, Scopus and Google Scholar with no limitation on the period of data collection. METHODS: Walker and Avant's concept analysis method was used to outline attributes, antecedents, consequences, and empirical referents of the concept digital health equity. RESULTS: The main attribute of digital health equity is digital health technology that benefits everyone fairly. The antecedents include: (1) appropriate infrastructure; (2) cognitive abilities including digital literacy; (3) intersectionality of multiple vulnerabilities; (4) presence of the core ethical principles in healthcare; (5) digital accessibility with careful consideration of the social determinants of health; and (6) co-creation of digital health technologies. The main consequences are improved patient health outcomes and elimination of the digital divide. CONCLUSION: This analysis explored the concept of digital health equity as a means to promote positive health outcomes for equity-deserving groups, highlighting the critical role of nursing practice and research in addressing digital health disparities. IMPACT STATEMENT: This paper can have an impact on nursing practice, education and wider social and economic issues. First, various barriers encountered by patients when utilising digital health technologies can be understood. Second, clinicians can be encouraged to assess digital health equity, improve interventions for equity-deserving groups, and evaluate the effectiveness of digital health interventions to ensure they are equitable. In the context of educational implications, the understanding of digital health equity can be used to facilitate the creation of appropriate education materials for clinicians. Finally, on a wider social and economic scale, understanding digital health equity can aid in the creation of policies to enable equitable digital health technologies. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution because this paper is a concept analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".