Key Challenges and Barriers to Digital Literacy for Older Adults: A Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite rising internet use in Canada, older adults continue to face significant barriers in adopting and using digital technologies. Digital literacy among older adults extends beyond technical proficiency, encompassing adaptation to new technologies, overcoming age-related limitations, and addressing socio-economic disparities. Limited digital skills hinder social participation, access to essential services, and engagement with eHealth technologies, exacerbating disparities in health outcomes. OBJECTIVE This scoping review aimed to identify key barriers to digital literacy among older adults, synthesizing evidence from existing literature to provide a comprehensive understanding of the challenges older adults face in adopting and utilizing digital technologies. METHODS A systematic search was conducted across PubMed and Ovid MEDLINE(R) databases, identifying qualitative, quantitative, and mixed-methods studies that explored digital literacy barriers among individuals aged 55 and older. Data extraction captured participant characteristics, study settings, methodologies, and key findings, which were synthesized into thematic categories. RESULTS A total of 19 studies met the inclusion criteria. Thematic analysis identified seven primary barriers to digital literacy among older adults: (1) Health Barriers (physical and cognitive limitations), (2) Support Networks (lack of social support and influence), (3) Convenience and Ease of Use (interface complexity and technological change), (4) Knowledge and Information (low digital literacy and limited awareness), (5) Perception Barriers (security concerns, self-efficacy, and preference for traditional methods), (6) Resource Barriers (limited access, financial constraints, and lack of training), and (7) Barriers for Special Populations. CONCLUSIONS The findings of this review underscore the necessity of a comprehensive, multi-faceted approach to addressing digital literacy barriers among older adults. Interventions should be tailored to accommodate physical and cognitive limitations, provide social and educational support, and ensure accessibility and affordability of digital tools. Future research should prioritize standardized definitions of digital literacy, evaluate long-term impacts of digital inclusion programs, and explore targeted strategies for vulnerable subpopulations. Policymakers, practitioners, and technology developers must collaborate to bridge the digital divide and promote digital inclusion among older adults, ensuring equitable access to information, healthcare, and social engagement in an increasingly digital society.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".