eHealth and the Digital Divide Among Older Canadians: Insights from a National Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The multi-disciplinary life course theory emphasizes the relation between a person's choices and their socio-economic context and their capacity to make decisions within existing opportunities/constraints. Older age is particularly characterized by social and environmental conditions that may impact people's use of technology and eHealth applications. OBJECTIVE: This research aims to present an overview of eHealth applications use among older Canadian adults and examine the relationship between eHealth use and social and health system interaction determinants. METHODS: We conducted a national cross-sectional survey of older adults (n=2000) in Canada assessing their technology (e.g., tablets, computers etc.) and eHealth applications (e.g., fall detection and telemonitoring technologies, Internet etc.) use, social determinants (e.g., socio-demographic characteristics, environmental living conditions) and health system interaction (e.g., health status, access to care, and services utilization). RESULTS: There is technological readiness (85% owned computers, 74% used Internet daily/weekly) among older Canadian adults, although it does not translate into eHealth applications use. Internet use to connect with health care professionals, access results/patient portals, or book medical appointment was limited. The use of telemonitoring, and fall detection technologies was low (around 9%, and 4%, respectively). There were significant variations in eHealth use highlighting the importance of accounting for social determinants and interactions with the healthcare system. 12.7% of the variance in online access to laboratory results was explained by the province of residence (higher in Ontario and British Columbia), living environment (lower in rural settings), and access/need variables (higher for those with private insurance and willingness to pay for quicker access, and hospitalized). Women reported more Internet use for self-diagnosis and looking for online information. Individuals with excellent perceived health, and those with no recent emergency visits or home care services (OR=2.16 and 3.427, CI=[1.23-3.80] and [1.55-7.60]), showed greater use of mApps for health and FDT, respectively. A digital divide exists within the older adults population that raises concerns about whether those with higher needs and limited resources have access to and can benefit from eHealth applications. CONCLUSIONS: Addressing the digital health gap among older adults is not simply a matter of technological access but of health equity and system sustainability. Without deliberate policies, digital health risks reinforcing existing disparities by disproportionately excluding those with the greatest health needs and the fewest resources. Our findings identify the groups most at risk of digital exclusion, such as rural residents, institutionalized older adults, and those with limited financial or insurance coverage, and point to where interventions can yield the greatest benefit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".