Navigating the Digital Chasm: Digital Inequity and the Determinants among Racialized Seniors in Calgary, Alberta.
Bibliographic record
Abstract
This community-engaged study, conducted by the University of Calgary in partnership with The City of Calgary, examines digital inequity among racialized and immigrant seniors in Calgary. With one-third of Calgary’s population comprised of landed immigrants and a growing number of older adults aging in place, understanding the multidimensional nature of digital exclusion is critical to advancing the City’s Digital Equity Strategy. The study aimed to identify barriers, patterns, and underlying factors shaping digital inclusion in order to inform targeted and actionable interventions. Funded by the Social Sciences and Humanities Research Council and approved by the University of Calgary’s Conjoint Health Research Ethics Board, the research used co-designed methods aligned with municipal priorities. A total of 394 seniors aged 65+ participated in surveys administered in nine languages through partnerships with community-based organizations. The sample was predominantly female (66%), largely aged 70–80 (57%), and primarily Canadian citizens or permanent residents (94%), with Southeast Asian participants forming the largest ethnic group (50%). Findings reveal a complex landscape of digital engagement. While attitudes toward technology were overwhelmingly positive—over 80% found technology useful and appealing—significant barriers persist. Limited awareness of available supports (38%), need for assistance navigating online services (25%), affordability challenges (15%), and accessibility concerns (19%) were prominent. Socioeconomic constraints were notable, with over half reporting annual incomes below $45,000 despite relatively high levels of education. Gender differences also emerged, with women reporting lower confidence and higher levels of fear in using technology. Importantly, more than half of participants experienced apprehension or hesitation when using digital tools, indicating that gerontechnology anxiety represents a meaningful barrier beyond infrastructure or skills alone. Overall, digital inequity among racialized seniors reflects intersecting socioeconomic, educational, gendered, and psychological factors. Effective digital inclusion strategies must therefore address not only access and affordability, but also literacy, culturally responsive outreach, and emotional barriers to technology use to ensure equitable participation and improved quality of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| 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".