Examining Practices Related to Ethical Aspects in eHealth Evaluation Research: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: eHealth technologies, including remote patient monitoring (RPM) applications, have the potential to improve care for diseases such as cancer and cardiovascular conditions. However, they also raise ethical aspects that are often inadequately addressed in eHealth evaluation research. This is problematic, as evaluations guide decision-making at multiple levels. To improve evaluation practices, it is essential to understand how ethical aspects are addressed in terms of both content and methodology, enabling the development of tailored recommendations for enhancement. OBJECTIVE: This scoping review systematically examines how ethical aspects are addressed in eHealth research, focusing on original studies evaluating RPM applications for cancer and cardiovascular diseases. METHODS: Using Joanna Briggs Institute (JBI) methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this review implemented a comprehensive search strategy with the terms "cancer or cardiovascular diseases," "eHealth or telemonitoring," and "evaluation designs." Searches included MEDLINE, Embase, CINAHL, SocINDEX, Philosopher's Index, PsycINFO, and Google Scholar. Data extraction will emphasize ethical aspects and methodological approaches to consider them. The analysis will apply inductive-deductive qualitative content analysis. RESULTS: Initial searches identified 3321 articles published between 2014 and August 2024. Screening and analysis will be completed in the first quarter of 2025, with results anticipated by summer 2025. CONCLUSIONS: Overlooking ethical aspects in evaluation studies can significantly impact eHealth practices. This scoping review will map ethical considerations in original evaluation research, identifying opportunities for more holistic integration of ethics and informing future practical guidance. TRIAL REGISTRATION: OSF Registries OSF.IO/7XAFV; https://osf.io/7xafv/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/60849.
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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 | 0.199 | 0.193 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".