Trust Barriers and Vulnerabilities in Older Adults’ Telemedicine Adoption: Scoping Review (Preprint)
Bibliographic record
Abstract
Background: Aging populations worldwide face increasing health care demands, particularly for chronic disease management. While telemedicine offers a viable solution to enhance health care access, significant trust-related barriers hinder its adoption among older adults, warranting a scoping synthesis of available evidence. Objective: This scoping review aims to map the available evidence on trust barriers experienced by older adults in telemedicine, identify underlying vulnerability domains, and chart the evidence base for design and policy recommendations. Methods: We conducted a scoping review in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, analyzing literature from PubMed, Web of Science, and Scopus. Thematic analysis was applied to synthesize findings from 30 included studies. Results: Four primary trust barriers were identified: technophobia and technical difficulties, privacy and data security concerns, negative emotional and social impacts, and a strong preference for in-person care. These barriers mapped onto 4 vulnerability domains: limited telemedicine literacy (particularly low eHealth self-efficacy), declining health status (including sensory and cognitive impairments), psychological and cognitive factors (such as anxiety about losing autonomy), and inadequate social support systems. The review also underscored how rapid technological change amplifies these challenges for older adults. Conclusions: Effective telemedicine implementation for older adults requires multipronged interventions, including age-appropriate interface design, targeted digital literacy training, robust privacy protections, and personalized support systems. These approaches address both technological and psychosocial barriers, potentially increasing engagement while mitigating vulnerabilities. Future research should assess the effectiveness of these interventions across diverse older populations.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".