Bridging the Digital Divide: Senior Chinese Immigrants’ Experiences with Health Technologies in Canada
Bibliographic record
Abstract
The global aging population is increasingly impacted by the digital divide, which affects their quality of life and access to services. Chinese senior immigrants in Canada face great challenges navigating digital environments due to language barriers, cultural adaptation issues, and limited access to digital resources. Using Cultural-Historical Activity Theory (CHAT) as a theoretical framework, this study explores the key factors of the digital divide among Chinese senior immigrants, challenges of their use of health-related technologies, and their strategies in adapting to these digital tools. Through semi-structured, in-depth interviews with 9 Chinese senior immigrants in Canada, this study argues that the digital divide is not merely an issue of technological access but a socially situated, culturally mediated, and historically developed phenomenon shaped by social structures, community supports, learning, and well-being. By framing digital literacy as a socially situated and developmental process, this study contributes to health communication by proposing a Cross-Cultural Digital Health Literacy Nexus (CDHLN) that enhances cross-cultural digital health engagement, improves senior immigrants' access to health information, and promotes well-being among aging immigrant populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".